Load scripts: loads libraries and useful scripts used in the analyses; all .R files contained in scripts at the root of the factory are automatically loaded
Load data: imports datasets, and may contain some ad hoc changes to the data such as specific data cleaning (not used in other reports), new variables used in the analyses, etc.
library(reportfactory)
library(here)
library(rio)
library(tidyverse)
library(incidence)
library(distcrete)
library(epitrix)
library(earlyR)
library(projections)
library(linelist)
library(remotes)
library(janitor)
library(kableExtra)
library(DT)
library(cyphr)
library(chngpt)
library(lubridate)
library(ggpubr)
library(ggnewscale)These scripts will load:
.R files inside /scripts/.R files inside /src/These scripts also contain routines to access the latest clean encrypted data (see next section).
We import the latest NHS pathways data:
x <- import_pathways() %>%
as_tibble()
x
## [90m# A tibble: 323,147 x 11[39m
## site_type date sex age ccg_code ccg_name count postcode nhs_region
## [3m[90m<chr>[39m[23m [3m[90m<date>[39m[23m [3m[90m<chr>[39m[23m [3m[90m<chr>[39m[23m [3m[90m<chr>[39m[23m [3m[90m<chr>[39m[23m [3m[90m<int>[39m[23m [3m[90m<chr>[39m[23m [3m[90m<chr>[39m[23m
## [90m 1[39m 111 2020-03-18 fema… miss… e380000… nhs_glo… 1 gl34fe South West
## [90m 2[39m 111 2020-03-18 fema… miss… e380001… nhs_sou… 1 ne325nn North Eas…
## [90m 3[39m 111 2020-03-18 fema… 0-18 e380000… nhs_air… 8 bd57jr North Eas…
## [90m 4[39m 111 2020-03-18 fema… 0-18 e380000… nhs_ash… 7 tn254ab South East
## [90m 5[39m 111 2020-03-18 fema… 0-18 e380000… nhs_bar… 35 rm13ae London
## [90m 6[39m 111 2020-03-18 fema… 0-18 e380000… nhs_bar… 9 n111np London
## [90m 7[39m 111 2020-03-18 fema… 0-18 e380000… nhs_bar… 11 s752py North Eas…
## [90m 8[39m 111 2020-03-18 fema… 0-18 e380000… nhs_bas… 19 ss143hg East of E…
## [90m 9[39m 111 2020-03-18 fema… 0-18 e380000… nhs_bas… 6 dn227xf North Eas…
## [90m10[39m 111 2020-03-18 fema… 0-18 e380000… nhs_bat… 9 ba25rp South West
## [90m# … with 323,137 more rows, and 2 more variables: day [3m[90m<int>[90m[23m, weekday [3m[90m<fct>[90m[23m[39mWe also import demographics data for NHS regions in England, used later in our analysis:
path <- here::here("data", "csv", "nhs_region_population_2018.csv")
nhs_region_pop <- rio::import(path) %>%
mutate(nhs_region = str_to_title(gsub("_"," ",nhs_region)))
nhs_region_pop$nhs_region <- gsub(" Of ", " of ", nhs_region_pop$nhs_region)
nhs_region_pop$nhs_region <- gsub(" And ", " and ", nhs_region_pop$nhs_region)
nhs_region_pop
## nhs_region variable value
## 1 North West 0-18 0.22538599
## 2 North East and Yorkshire 0-18 0.21876449
## 3 Midlands 0-18 0.22564656
## 4 East of England 0-18 0.22810783
## 5 London 0-18 0.23764782
## 6 South East 0-18 0.22458811
## 7 South West 0-18 0.20799797
## 8 North West 19-69 0.64274078
## 9 North East and Yorkshire 19-69 0.64437753
## 10 Midlands 19-69 0.63876675
## 11 East of England 19-69 0.63034229
## 12 London 19-69 0.67820084
## 13 South East 19-69 0.63267336
## 14 South West 19-69 0.63176131
## 15 North West 70-120 0.13187323
## 16 North East and Yorkshire 70-120 0.13685797
## 17 Midlands 70-120 0.13558669
## 18 East of England 70-120 0.14154988
## 19 London 70-120 0.08415135
## 20 South East 70-120 0.14273853
## 21 South West 70-120 0.16024072Finally, we import publically available deaths per NHS region:
dth <- import_deaths() %>%
mutate(nhs_region = str_to_title(gsub("_"," ",nhs_region)))
#truncation to account for reporting delay
delay_max <- 21
dth$nhs_region <- gsub(" Of ", " of ", dth$nhs_region)
dth$nhs_region <- gsub(" And ", " and ", dth$nhs_region)
dth
## date_report nhs_region deaths
## 1 2020-03-01 East of England 0
## 2 2020-03-02 East of England 1
## 3 2020-03-03 East of England 0
## 4 2020-03-04 East of England 0
## 5 2020-03-05 East of England 0
## 6 2020-03-06 East of England 1
## 7 2020-03-07 East of England 0
## 8 2020-03-08 East of England 0
## 9 2020-03-09 East of England 1
## 10 2020-03-10 East of England 0
## 11 2020-03-11 East of England 0
## 12 2020-03-12 East of England 0
## 13 2020-03-13 East of England 1
## 14 2020-03-14 East of England 2
## 15 2020-03-15 East of England 2
## 16 2020-03-16 East of England 1
## 17 2020-03-17 East of England 1
## 18 2020-03-18 East of England 5
## 19 2020-03-19 East of England 4
## 20 2020-03-20 East of England 2
## 21 2020-03-21 East of England 11
## 22 2020-03-22 East of England 12
## 23 2020-03-23 East of England 11
## 24 2020-03-24 East of England 19
## 25 2020-03-25 East of England 26
## 26 2020-03-26 East of England 36
## 27 2020-03-27 East of England 38
## 28 2020-03-28 East of England 28
## 29 2020-03-29 East of England 43
## 30 2020-03-30 East of England 45
## 31 2020-03-31 East of England 70
## 32 2020-04-01 East of England 62
## 33 2020-04-02 East of England 65
## 34 2020-04-03 East of England 80
## 35 2020-04-04 East of England 71
## 36 2020-04-05 East of England 76
## 37 2020-04-06 East of England 71
## 38 2020-04-07 East of England 93
## 39 2020-04-08 East of England 111
## 40 2020-04-09 East of England 87
## 41 2020-04-10 East of England 74
## 42 2020-04-11 East of England 92
## 43 2020-04-12 East of England 100
## 44 2020-04-13 East of England 78
## 45 2020-04-14 East of England 61
## 46 2020-04-15 East of England 82
## 47 2020-04-16 East of England 74
## 48 2020-04-17 East of England 86
## 49 2020-04-18 East of England 64
## 50 2020-04-19 East of England 67
## 51 2020-04-20 East of England 67
## 52 2020-04-21 East of England 75
## 53 2020-04-22 East of England 67
## 54 2020-04-23 East of England 49
## 55 2020-04-24 East of England 66
## 56 2020-04-25 East of England 54
## 57 2020-04-26 East of England 48
## 58 2020-04-27 East of England 46
## 59 2020-04-28 East of England 58
## 60 2020-04-29 East of England 32
## 61 2020-04-30 East of England 45
## 62 2020-05-01 East of England 49
## 63 2020-05-02 East of England 29
## 64 2020-05-03 East of England 41
## 65 2020-05-04 East of England 19
## 66 2020-05-05 East of England 36
## 67 2020-05-06 East of England 31
## 68 2020-05-07 East of England 33
## 69 2020-05-08 East of England 33
## 70 2020-05-09 East of England 29
## 71 2020-05-10 East of England 22
## 72 2020-05-11 East of England 18
## 73 2020-05-12 East of England 21
## 74 2020-05-13 East of England 27
## 75 2020-05-14 East of England 26
## 76 2020-05-15 East of England 19
## 77 2020-05-16 East of England 26
## 78 2020-05-17 East of England 17
## 79 2020-05-18 East of England 25
## 80 2020-05-19 East of England 15
## 81 2020-05-20 East of England 26
## 82 2020-05-21 East of England 21
## 83 2020-05-22 East of England 13
## 84 2020-05-23 East of England 12
## 85 2020-05-24 East of England 17
## 86 2020-05-25 East of England 25
## 87 2020-05-26 East of England 14
## 88 2020-05-27 East of England 12
## 89 2020-05-28 East of England 17
## 90 2020-05-29 East of England 16
## 91 2020-05-30 East of England 9
## 92 2020-05-31 East of England 8
## 93 2020-06-01 East of England 17
## 94 2020-06-02 East of England 14
## 95 2020-06-03 East of England 10
## 96 2020-06-04 East of England 7
## 97 2020-06-05 East of England 14
## 98 2020-06-06 East of England 5
## 99 2020-06-07 East of England 9
## 100 2020-06-08 East of England 7
## 101 2020-06-09 East of England 6
## 102 2020-06-10 East of England 8
## 103 2020-06-11 East of England 1
## 104 2020-06-12 East of England 9
## 105 2020-06-13 East of England 5
## 106 2020-06-14 East of England 4
## 107 2020-06-15 East of England 8
## 108 2020-06-16 East of England 3
## 109 2020-06-17 East of England 7
## 110 2020-06-18 East of England 4
## 111 2020-06-19 East of England 7
## 112 2020-06-20 East of England 4
## 113 2020-06-21 East of England 3
## 114 2020-06-22 East of England 6
## 115 2020-06-23 East of England 5
## 116 2020-06-24 East of England 4
## 117 2020-06-25 East of England 1
## 118 2020-06-26 East of England 5
## 119 2020-06-27 East of England 6
## 120 2020-06-28 East of England 8
## 121 2020-06-29 East of England 4
## 122 2020-06-30 East of England 5
## 123 2020-07-01 East of England 2
## 124 2020-07-02 East of England 5
## 125 2020-07-03 East of England 0
## 126 2020-07-04 East of England 3
## 127 2020-07-05 East of England 1
## 128 2020-07-06 East of England 2
## 129 2020-07-07 East of England 2
## 130 2020-07-08 East of England 0
## 131 2020-07-09 East of England 8
## 132 2020-07-10 East of England 4
## 133 2020-07-11 East of England 2
## 134 2020-07-12 East of England 1
## 135 2020-07-13 East of England 8
## 136 2020-07-14 East of England 2
## 137 2020-07-15 East of England 0
## 138 2020-07-16 East of England 0
## 139 2020-07-17 East of England 0
## 140 2020-07-18 East of England 0
## 141 2020-07-19 East of England 1
## 142 2020-07-20 East of England 1
## 143 2020-07-21 East of England 1
## 144 2020-07-22 East of England 2
## 145 2020-07-23 East of England 1
## 146 2020-07-24 East of England 1
## 147 2020-07-25 East of England 0
## 148 2020-07-26 East of England 1
## 149 2020-07-27 East of England 1
## 150 2020-07-28 East of England 2
## 151 2020-07-29 East of England 0
## 152 2020-07-30 East of England 0
## 153 2020-07-31 East of England 1
## 154 2020-08-01 East of England 0
## 155 2020-08-02 East of England 0
## 156 2020-08-03 East of England 0
## 157 2020-08-04 East of England 1
## 158 2020-08-05 East of England 1
## 159 2020-08-06 East of England 0
## 160 2020-08-07 East of England 1
## 161 2020-08-08 East of England 0
## 162 2020-08-09 East of England 0
## 163 2020-08-10 East of England 1
## 164 2020-08-11 East of England 2
## 165 2020-08-12 East of England 1
## 166 2020-08-13 East of England 0
## 167 2020-08-14 East of England 1
## 168 2020-08-15 East of England 1
## 169 2020-08-16 East of England 0
## 170 2020-08-17 East of England 0
## 171 2020-08-18 East of England 2
## 172 2020-08-19 East of England 1
## 173 2020-08-20 East of England 1
## 174 2020-08-21 East of England 0
## 175 2020-08-22 East of England 1
## 176 2020-08-23 East of England 1
## 177 2020-08-24 East of England 0
## 178 2020-08-25 East of England 0
## 179 2020-08-26 East of England 1
## 180 2020-08-27 East of England 1
## 181 2020-08-28 East of England 0
## 182 2020-08-29 East of England 0
## 183 2020-08-30 East of England 0
## 184 2020-08-31 East of England 0
## 185 2020-09-01 East of England 0
## 186 2020-09-02 East of England 0
## 187 2020-09-03 East of England 1
## 188 2020-09-04 East of England 1
## 189 2020-09-05 East of England 0
## 190 2020-09-06 East of England 1
## 191 2020-09-07 East of England 0
## 192 2020-09-08 East of England 0
## 193 2020-09-09 East of England 0
## 194 2020-09-10 East of England 0
## 195 2020-09-11 East of England 0
## 196 2020-09-12 East of England 0
## 197 2020-09-13 East of England 1
## 198 2020-09-14 East of England 1
## 199 2020-09-15 East of England 0
## 200 2020-09-16 East of England 0
## 201 2020-09-17 East of England 0
## 202 2020-09-18 East of England 0
## 203 2020-09-19 East of England 0
## 204 2020-09-20 East of England 2
## 205 2020-09-21 East of England 0
## 206 2020-09-22 East of England 2
## 207 2020-09-23 East of England 1
## 208 2020-09-24 East of England 0
## 209 2020-09-25 East of England 1
## 210 2020-09-26 East of England 1
## 211 2020-09-27 East of England 1
## 212 2020-09-28 East of England 2
## 213 2020-09-29 East of England 2
## 214 2020-09-30 East of England 2
## 215 2020-10-01 East of England 2
## 216 2020-10-02 East of England 1
## 217 2020-10-03 East of England 1
## 218 2020-10-04 East of England 0
## 219 2020-10-05 East of England 0
## 220 2020-10-06 East of England 4
## 221 2020-10-07 East of England 6
## 222 2020-10-08 East of England 3
## 223 2020-10-09 East of England 1
## 224 2020-10-10 East of England 6
## 225 2020-10-11 East of England 2
## 226 2020-10-12 East of England 2
## 227 2020-10-13 East of England 1
## 228 2020-10-14 East of England 3
## 229 2020-10-15 East of England 4
## 230 2020-10-16 East of England 5
## 231 2020-10-17 East of England 6
## 232 2020-10-18 East of England 7
## 233 2020-10-19 East of England 5
## 234 2020-10-20 East of England 9
## 235 2020-10-21 East of England 7
## 236 2020-10-22 East of England 7
## 237 2020-10-23 East of England 14
## 238 2020-10-24 East of England 1
## 239 2020-10-25 East of England 10
## 240 2020-10-26 East of England 10
## 241 2020-10-27 East of England 6
## 242 2020-10-28 East of England 12
## 243 2020-10-29 East of England 10
## 244 2020-10-30 East of England 12
## 245 2020-10-31 East of England 15
## 246 2020-11-01 East of England 14
## 247 2020-11-02 East of England 8
## 248 2020-11-03 East of England 14
## 249 2020-11-04 East of England 10
## 250 2020-11-05 East of England 10
## 251 2020-11-06 East of England 17
## 252 2020-11-07 East of England 10
## 253 2020-11-08 East of England 13
## 254 2020-11-09 East of England 12
## 255 2020-11-10 East of England 24
## 256 2020-11-11 East of England 12
## 257 2020-11-12 East of England 10
## 258 2020-11-13 East of England 4
## 259 2020-03-01 London 0
## 260 2020-03-02 London 0
## 261 2020-03-03 London 0
## 262 2020-03-04 London 0
## 263 2020-03-05 London 0
## 264 2020-03-06 London 1
## 265 2020-03-07 London 0
## 266 2020-03-08 London 0
## 267 2020-03-09 London 1
## 268 2020-03-10 London 0
## 269 2020-03-11 London 5
## 270 2020-03-12 London 6
## 271 2020-03-13 London 10
## 272 2020-03-14 London 13
## 273 2020-03-15 London 9
## 274 2020-03-16 London 15
## 275 2020-03-17 London 23
## 276 2020-03-18 London 28
## 277 2020-03-19 London 25
## 278 2020-03-20 London 44
## 279 2020-03-21 London 49
## 280 2020-03-22 London 54
## 281 2020-03-23 London 63
## 282 2020-03-24 London 86
## 283 2020-03-25 London 112
## 284 2020-03-26 London 129
## 285 2020-03-27 London 130
## 286 2020-03-28 London 123
## 287 2020-03-29 London 145
## 288 2020-03-30 London 151
## 289 2020-03-31 London 183
## 290 2020-04-01 London 202
## 291 2020-04-02 London 191
## 292 2020-04-03 London 199
## 293 2020-04-04 London 231
## 294 2020-04-05 London 195
## 295 2020-04-06 London 198
## 296 2020-04-07 London 220
## 297 2020-04-08 London 239
## 298 2020-04-09 London 207
## 299 2020-04-10 London 171
## 300 2020-04-11 London 178
## 301 2020-04-12 London 158
## 302 2020-04-13 London 166
## 303 2020-04-14 London 143
## 304 2020-04-15 London 143
## 305 2020-04-16 London 140
## 306 2020-04-17 London 101
## 307 2020-04-18 London 101
## 308 2020-04-19 London 103
## 309 2020-04-20 London 96
## 310 2020-04-21 London 96
## 311 2020-04-22 London 109
## 312 2020-04-23 London 77
## 313 2020-04-24 London 71
## 314 2020-04-25 London 58
## 315 2020-04-26 London 53
## 316 2020-04-27 London 52
## 317 2020-04-28 London 44
## 318 2020-04-29 London 45
## 319 2020-04-30 London 40
## 320 2020-05-01 London 41
## 321 2020-05-02 London 41
## 322 2020-05-03 London 36
## 323 2020-05-04 London 30
## 324 2020-05-05 London 25
## 325 2020-05-06 London 37
## 326 2020-05-07 London 37
## 327 2020-05-08 London 30
## 328 2020-05-09 London 23
## 329 2020-05-10 London 26
## 330 2020-05-11 London 18
## 331 2020-05-12 London 18
## 332 2020-05-13 London 17
## 333 2020-05-14 London 20
## 334 2020-05-15 London 19
## 335 2020-05-16 London 14
## 336 2020-05-17 London 15
## 337 2020-05-18 London 11
## 338 2020-05-19 London 14
## 339 2020-05-20 London 19
## 340 2020-05-21 London 12
## 341 2020-05-22 London 10
## 342 2020-05-23 London 6
## 343 2020-05-24 London 7
## 344 2020-05-25 London 9
## 345 2020-05-26 London 14
## 346 2020-05-27 London 7
## 347 2020-05-28 London 8
## 348 2020-05-29 London 7
## 349 2020-05-30 London 12
## 350 2020-05-31 London 6
## 351 2020-06-01 London 10
## 352 2020-06-02 London 8
## 353 2020-06-03 London 6
## 354 2020-06-04 London 8
## 355 2020-06-05 London 4
## 356 2020-06-06 London 0
## 357 2020-06-07 London 5
## 358 2020-06-08 London 5
## 359 2020-06-09 London 5
## 360 2020-06-10 London 8
## 361 2020-06-11 London 5
## 362 2020-06-12 London 3
## 363 2020-06-13 London 3
## 364 2020-06-14 London 3
## 365 2020-06-15 London 1
## 366 2020-06-16 London 2
## 367 2020-06-17 London 1
## 368 2020-06-18 London 2
## 369 2020-06-19 London 5
## 370 2020-06-20 London 3
## 371 2020-06-21 London 4
## 372 2020-06-22 London 2
## 373 2020-06-23 London 1
## 374 2020-06-24 London 4
## 375 2020-06-25 London 3
## 376 2020-06-26 London 2
## 377 2020-06-27 London 1
## 378 2020-06-28 London 2
## 379 2020-06-29 London 2
## 380 2020-06-30 London 1
## 381 2020-07-01 London 3
## 382 2020-07-02 London 2
## 383 2020-07-03 London 2
## 384 2020-07-04 London 1
## 385 2020-07-05 London 3
## 386 2020-07-06 London 2
## 387 2020-07-07 London 1
## 388 2020-07-08 London 3
## 389 2020-07-09 London 4
## 390 2020-07-10 London 0
## 391 2020-07-11 London 1
## 392 2020-07-12 London 1
## 393 2020-07-13 London 1
## 394 2020-07-14 London 0
## 395 2020-07-15 London 2
## 396 2020-07-16 London 0
## 397 2020-07-17 London 0
## 398 2020-07-18 London 2
## 399 2020-07-19 London 0
## 400 2020-07-20 London 0
## 401 2020-07-21 London 1
## 402 2020-07-22 London 0
## 403 2020-07-23 London 2
## 404 2020-07-24 London 0
## 405 2020-07-25 London 1
## 406 2020-07-26 London 0
## 407 2020-07-27 London 1
## 408 2020-07-28 London 0
## 409 2020-07-29 London 0
## 410 2020-07-30 London 1
## 411 2020-07-31 London 0
## 412 2020-08-01 London 0
## 413 2020-08-02 London 3
## 414 2020-08-03 London 0
## 415 2020-08-04 London 0
## 416 2020-08-05 London 0
## 417 2020-08-06 London 1
## 418 2020-08-07 London 0
## 419 2020-08-08 London 0
## 420 2020-08-09 London 0
## 421 2020-08-10 London 0
## 422 2020-08-11 London 1
## 423 2020-08-12 London 0
## 424 2020-08-13 London 2
## 425 2020-08-14 London 0
## 426 2020-08-15 London 0
## 427 2020-08-16 London 0
## 428 2020-08-17 London 1
## 429 2020-08-18 London 1
## 430 2020-08-19 London 0
## 431 2020-08-20 London 1
## 432 2020-08-21 London 0
## 433 2020-08-22 London 0
## 434 2020-08-23 London 0
## 435 2020-08-24 London 1
## 436 2020-08-25 London 1
## 437 2020-08-26 London 0
## 438 2020-08-27 London 0
## 439 2020-08-28 London 0
## 440 2020-08-29 London 0
## 441 2020-08-30 London 0
## 442 2020-08-31 London 1
## 443 2020-09-01 London 0
## 444 2020-09-02 London 1
## 445 2020-09-03 London 1
## 446 2020-09-04 London 0
## 447 2020-09-05 London 0
## 448 2020-09-06 London 2
## 449 2020-09-07 London 0
## 450 2020-09-08 London 0
## 451 2020-09-09 London 0
## 452 2020-09-10 London 2
## 453 2020-09-11 London 1
## 454 2020-09-12 London 1
## 455 2020-09-13 London 0
## 456 2020-09-14 London 0
## 457 2020-09-15 London 1
## 458 2020-09-16 London 2
## 459 2020-09-17 London 2
## 460 2020-09-18 London 1
## 461 2020-09-19 London 3
## 462 2020-09-20 London 3
## 463 2020-09-21 London 2
## 464 2020-09-22 London 6
## 465 2020-09-23 London 4
## 466 2020-09-24 London 3
## 467 2020-09-25 London 1
## 468 2020-09-26 London 1
## 469 2020-09-27 London 1
## 470 2020-09-28 London 3
## 471 2020-09-29 London 7
## 472 2020-09-30 London 6
## 473 2020-10-01 London 4
## 474 2020-10-02 London 1
## 475 2020-10-03 London 3
## 476 2020-10-04 London 2
## 477 2020-10-05 London 7
## 478 2020-10-06 London 4
## 479 2020-10-07 London 6
## 480 2020-10-08 London 6
## 481 2020-10-09 London 7
## 482 2020-10-10 London 3
## 483 2020-10-11 London 5
## 484 2020-10-12 London 7
## 485 2020-10-13 London 4
## 486 2020-10-14 London 5
## 487 2020-10-15 London 13
## 488 2020-10-16 London 6
## 489 2020-10-17 London 2
## 490 2020-10-18 London 5
## 491 2020-10-19 London 11
## 492 2020-10-20 London 8
## 493 2020-10-21 London 14
## 494 2020-10-22 London 12
## 495 2020-10-23 London 7
## 496 2020-10-24 London 18
## 497 2020-10-25 London 10
## 498 2020-10-26 London 10
## 499 2020-10-27 London 12
## 500 2020-10-28 London 22
## 501 2020-10-29 London 14
## 502 2020-10-30 London 17
## 503 2020-10-31 London 7
## 504 2020-11-01 London 16
## 505 2020-11-02 London 15
## 506 2020-11-03 London 10
## 507 2020-11-04 London 17
## 508 2020-11-05 London 17
## 509 2020-11-06 London 12
## 510 2020-11-07 London 20
## 511 2020-11-08 London 12
## 512 2020-11-09 London 25
## 513 2020-11-10 London 14
## 514 2020-11-11 London 13
## 515 2020-11-12 London 10
## 516 2020-11-13 London 0
## 517 2020-03-01 Midlands 0
## 518 2020-03-02 Midlands 0
## 519 2020-03-03 Midlands 1
## 520 2020-03-04 Midlands 0
## 521 2020-03-05 Midlands 0
## 522 2020-03-06 Midlands 0
## 523 2020-03-07 Midlands 0
## 524 2020-03-08 Midlands 2
## 525 2020-03-09 Midlands 1
## 526 2020-03-10 Midlands 0
## 527 2020-03-11 Midlands 2
## 528 2020-03-12 Midlands 6
## 529 2020-03-13 Midlands 5
## 530 2020-03-14 Midlands 4
## 531 2020-03-15 Midlands 5
## 532 2020-03-16 Midlands 11
## 533 2020-03-17 Midlands 8
## 534 2020-03-18 Midlands 13
## 535 2020-03-19 Midlands 8
## 536 2020-03-20 Midlands 28
## 537 2020-03-21 Midlands 13
## 538 2020-03-22 Midlands 31
## 539 2020-03-23 Midlands 33
## 540 2020-03-24 Midlands 41
## 541 2020-03-25 Midlands 48
## 542 2020-03-26 Midlands 64
## 543 2020-03-27 Midlands 72
## 544 2020-03-28 Midlands 89
## 545 2020-03-29 Midlands 92
## 546 2020-03-30 Midlands 90
## 547 2020-03-31 Midlands 123
## 548 2020-04-01 Midlands 140
## 549 2020-04-02 Midlands 142
## 550 2020-04-03 Midlands 124
## 551 2020-04-04 Midlands 151
## 552 2020-04-05 Midlands 164
## 553 2020-04-06 Midlands 140
## 554 2020-04-07 Midlands 123
## 555 2020-04-08 Midlands 186
## 556 2020-04-09 Midlands 139
## 557 2020-04-10 Midlands 127
## 558 2020-04-11 Midlands 142
## 559 2020-04-12 Midlands 139
## 560 2020-04-13 Midlands 120
## 561 2020-04-14 Midlands 116
## 562 2020-04-15 Midlands 147
## 563 2020-04-16 Midlands 102
## 564 2020-04-17 Midlands 118
## 565 2020-04-18 Midlands 115
## 566 2020-04-19 Midlands 92
## 567 2020-04-20 Midlands 107
## 568 2020-04-21 Midlands 86
## 569 2020-04-22 Midlands 78
## 570 2020-04-23 Midlands 103
## 571 2020-04-24 Midlands 79
## 572 2020-04-25 Midlands 72
## 573 2020-04-26 Midlands 81
## 574 2020-04-27 Midlands 74
## 575 2020-04-28 Midlands 68
## 576 2020-04-29 Midlands 53
## 577 2020-04-30 Midlands 56
## 578 2020-05-01 Midlands 64
## 579 2020-05-02 Midlands 51
## 580 2020-05-03 Midlands 52
## 581 2020-05-04 Midlands 61
## 582 2020-05-05 Midlands 59
## 583 2020-05-06 Midlands 59
## 584 2020-05-07 Midlands 48
## 585 2020-05-08 Midlands 34
## 586 2020-05-09 Midlands 37
## 587 2020-05-10 Midlands 42
## 588 2020-05-11 Midlands 33
## 589 2020-05-12 Midlands 45
## 590 2020-05-13 Midlands 40
## 591 2020-05-14 Midlands 39
## 592 2020-05-15 Midlands 40
## 593 2020-05-16 Midlands 34
## 594 2020-05-17 Midlands 31
## 595 2020-05-18 Midlands 36
## 596 2020-05-19 Midlands 35
## 597 2020-05-20 Midlands 36
## 598 2020-05-21 Midlands 32
## 599 2020-05-22 Midlands 27
## 600 2020-05-23 Midlands 34
## 601 2020-05-24 Midlands 20
## 602 2020-05-25 Midlands 26
## 603 2020-05-26 Midlands 33
## 604 2020-05-27 Midlands 29
## 605 2020-05-28 Midlands 28
## 606 2020-05-29 Midlands 20
## 607 2020-05-30 Midlands 21
## 608 2020-05-31 Midlands 22
## 609 2020-06-01 Midlands 20
## 610 2020-06-02 Midlands 22
## 611 2020-06-03 Midlands 24
## 612 2020-06-04 Midlands 16
## 613 2020-06-05 Midlands 21
## 614 2020-06-06 Midlands 20
## 615 2020-06-07 Midlands 17
## 616 2020-06-08 Midlands 16
## 617 2020-06-09 Midlands 18
## 618 2020-06-10 Midlands 15
## 619 2020-06-11 Midlands 13
## 620 2020-06-12 Midlands 12
## 621 2020-06-13 Midlands 6
## 622 2020-06-14 Midlands 18
## 623 2020-06-15 Midlands 12
## 624 2020-06-16 Midlands 15
## 625 2020-06-17 Midlands 11
## 626 2020-06-18 Midlands 15
## 627 2020-06-19 Midlands 10
## 628 2020-06-20 Midlands 15
## 629 2020-06-21 Midlands 14
## 630 2020-06-22 Midlands 14
## 631 2020-06-23 Midlands 16
## 632 2020-06-24 Midlands 15
## 633 2020-06-25 Midlands 18
## 634 2020-06-26 Midlands 5
## 635 2020-06-27 Midlands 5
## 636 2020-06-28 Midlands 7
## 637 2020-06-29 Midlands 6
## 638 2020-06-30 Midlands 6
## 639 2020-07-01 Midlands 7
## 640 2020-07-02 Midlands 10
## 641 2020-07-03 Midlands 3
## 642 2020-07-04 Midlands 4
## 643 2020-07-05 Midlands 6
## 644 2020-07-06 Midlands 5
## 645 2020-07-07 Midlands 3
## 646 2020-07-08 Midlands 5
## 647 2020-07-09 Midlands 9
## 648 2020-07-10 Midlands 3
## 649 2020-07-11 Midlands 0
## 650 2020-07-12 Midlands 5
## 651 2020-07-13 Midlands 1
## 652 2020-07-14 Midlands 1
## 653 2020-07-15 Midlands 6
## 654 2020-07-16 Midlands 2
## 655 2020-07-17 Midlands 3
## 656 2020-07-18 Midlands 3
## 657 2020-07-19 Midlands 3
## 658 2020-07-20 Midlands 3
## 659 2020-07-21 Midlands 1
## 660 2020-07-22 Midlands 2
## 661 2020-07-23 Midlands 6
## 662 2020-07-24 Midlands 1
## 663 2020-07-25 Midlands 4
## 664 2020-07-26 Midlands 4
## 665 2020-07-27 Midlands 5
## 666 2020-07-28 Midlands 1
## 667 2020-07-29 Midlands 1
## 668 2020-07-30 Midlands 1
## 669 2020-07-31 Midlands 2
## 670 2020-08-01 Midlands 0
## 671 2020-08-02 Midlands 1
## 672 2020-08-03 Midlands 2
## 673 2020-08-04 Midlands 1
## 674 2020-08-05 Midlands 1
## 675 2020-08-06 Midlands 0
## 676 2020-08-07 Midlands 3
## 677 2020-08-08 Midlands 2
## 678 2020-08-09 Midlands 0
## 679 2020-08-10 Midlands 0
## 680 2020-08-11 Midlands 2
## 681 2020-08-12 Midlands 0
## 682 2020-08-13 Midlands 0
## 683 2020-08-14 Midlands 0
## 684 2020-08-15 Midlands 1
## 685 2020-08-16 Midlands 0
## 686 2020-08-17 Midlands 0
## 687 2020-08-18 Midlands 0
## 688 2020-08-19 Midlands 0
## 689 2020-08-20 Midlands 0
## 690 2020-08-21 Midlands 1
## 691 2020-08-22 Midlands 0
## 692 2020-08-23 Midlands 0
## 693 2020-08-24 Midlands 0
## 694 2020-08-25 Midlands 2
## 695 2020-08-26 Midlands 3
## 696 2020-08-27 Midlands 2
## 697 2020-08-28 Midlands 1
## 698 2020-08-29 Midlands 0
## 699 2020-08-30 Midlands 2
## 700 2020-08-31 Midlands 1
## 701 2020-09-01 Midlands 0
## 702 2020-09-02 Midlands 2
## 703 2020-09-03 Midlands 0
## 704 2020-09-04 Midlands 0
## 705 2020-09-05 Midlands 0
## 706 2020-09-06 Midlands 1
## 707 2020-09-07 Midlands 1
## 708 2020-09-08 Midlands 3
## 709 2020-09-09 Midlands 0
## 710 2020-09-10 Midlands 1
## 711 2020-09-11 Midlands 1
## 712 2020-09-12 Midlands 2
## 713 2020-09-13 Midlands 4
## 714 2020-09-14 Midlands 1
## 715 2020-09-15 Midlands 2
## 716 2020-09-16 Midlands 3
## 717 2020-09-17 Midlands 2
## 718 2020-09-18 Midlands 5
## 719 2020-09-19 Midlands 2
## 720 2020-09-20 Midlands 7
## 721 2020-09-21 Midlands 3
## 722 2020-09-22 Midlands 4
## 723 2020-09-23 Midlands 10
## 724 2020-09-24 Midlands 7
## 725 2020-09-25 Midlands 4
## 726 2020-09-26 Midlands 5
## 727 2020-09-27 Midlands 9
## 728 2020-09-28 Midlands 6
## 729 2020-09-29 Midlands 4
## 730 2020-09-30 Midlands 5
## 731 2020-10-01 Midlands 8
## 732 2020-10-02 Midlands 7
## 733 2020-10-03 Midlands 6
## 734 2020-10-04 Midlands 7
## 735 2020-10-05 Midlands 6
## 736 2020-10-06 Midlands 5
## 737 2020-10-07 Midlands 9
## 738 2020-10-08 Midlands 8
## 739 2020-10-09 Midlands 7
## 740 2020-10-10 Midlands 2
## 741 2020-10-11 Midlands 15
## 742 2020-10-12 Midlands 7
## 743 2020-10-13 Midlands 16
## 744 2020-10-14 Midlands 12
## 745 2020-10-15 Midlands 11
## 746 2020-10-16 Midlands 18
## 747 2020-10-17 Midlands 25
## 748 2020-10-18 Midlands 11
## 749 2020-10-19 Midlands 14
## 750 2020-10-20 Midlands 19
## 751 2020-10-21 Midlands 15
## 752 2020-10-22 Midlands 34
## 753 2020-10-23 Midlands 32
## 754 2020-10-24 Midlands 24
## 755 2020-10-25 Midlands 29
## 756 2020-10-26 Midlands 32
## 757 2020-10-27 Midlands 36
## 758 2020-10-28 Midlands 30
## 759 2020-10-29 Midlands 41
## 760 2020-10-30 Midlands 41
## 761 2020-10-31 Midlands 50
## 762 2020-11-01 Midlands 44
## 763 2020-11-02 Midlands 57
## 764 2020-11-03 Midlands 33
## 765 2020-11-04 Midlands 65
## 766 2020-11-05 Midlands 48
## 767 2020-11-06 Midlands 43
## 768 2020-11-07 Midlands 59
## 769 2020-11-08 Midlands 52
## 770 2020-11-09 Midlands 63
## 771 2020-11-10 Midlands 67
## 772 2020-11-11 Midlands 54
## 773 2020-11-12 Midlands 55
## 774 2020-11-13 Midlands 11
## 775 2020-03-01 North East and Yorkshire 0
## 776 2020-03-02 North East and Yorkshire 0
## 777 2020-03-03 North East and Yorkshire 0
## 778 2020-03-04 North East and Yorkshire 0
## 779 2020-03-05 North East and Yorkshire 0
## 780 2020-03-06 North East and Yorkshire 0
## 781 2020-03-07 North East and Yorkshire 0
## 782 2020-03-08 North East and Yorkshire 0
## 783 2020-03-09 North East and Yorkshire 0
## 784 2020-03-10 North East and Yorkshire 0
## 785 2020-03-11 North East and Yorkshire 0
## 786 2020-03-12 North East and Yorkshire 0
## 787 2020-03-13 North East and Yorkshire 0
## 788 2020-03-14 North East and Yorkshire 0
## 789 2020-03-15 North East and Yorkshire 2
## 790 2020-03-16 North East and Yorkshire 3
## 791 2020-03-17 North East and Yorkshire 1
## 792 2020-03-18 North East and Yorkshire 2
## 793 2020-03-19 North East and Yorkshire 6
## 794 2020-03-20 North East and Yorkshire 5
## 795 2020-03-21 North East and Yorkshire 6
## 796 2020-03-22 North East and Yorkshire 7
## 797 2020-03-23 North East and Yorkshire 9
## 798 2020-03-24 North East and Yorkshire 8
## 799 2020-03-25 North East and Yorkshire 18
## 800 2020-03-26 North East and Yorkshire 21
## 801 2020-03-27 North East and Yorkshire 28
## 802 2020-03-28 North East and Yorkshire 35
## 803 2020-03-29 North East and Yorkshire 38
## 804 2020-03-30 North East and Yorkshire 64
## 805 2020-03-31 North East and Yorkshire 60
## 806 2020-04-01 North East and Yorkshire 67
## 807 2020-04-02 North East and Yorkshire 75
## 808 2020-04-03 North East and Yorkshire 100
## 809 2020-04-04 North East and Yorkshire 105
## 810 2020-04-05 North East and Yorkshire 92
## 811 2020-04-06 North East and Yorkshire 96
## 812 2020-04-07 North East and Yorkshire 102
## 813 2020-04-08 North East and Yorkshire 107
## 814 2020-04-09 North East and Yorkshire 111
## 815 2020-04-10 North East and Yorkshire 117
## 816 2020-04-11 North East and Yorkshire 98
## 817 2020-04-12 North East and Yorkshire 84
## 818 2020-04-13 North East and Yorkshire 94
## 819 2020-04-14 North East and Yorkshire 107
## 820 2020-04-15 North East and Yorkshire 96
## 821 2020-04-16 North East and Yorkshire 103
## 822 2020-04-17 North East and Yorkshire 88
## 823 2020-04-18 North East and Yorkshire 95
## 824 2020-04-19 North East and Yorkshire 88
## 825 2020-04-20 North East and Yorkshire 100
## 826 2020-04-21 North East and Yorkshire 76
## 827 2020-04-22 North East and Yorkshire 84
## 828 2020-04-23 North East and Yorkshire 63
## 829 2020-04-24 North East and Yorkshire 72
## 830 2020-04-25 North East and Yorkshire 69
## 831 2020-04-26 North East and Yorkshire 65
## 832 2020-04-27 North East and Yorkshire 65
## 833 2020-04-28 North East and Yorkshire 57
## 834 2020-04-29 North East and Yorkshire 69
## 835 2020-04-30 North East and Yorkshire 57
## 836 2020-05-01 North East and Yorkshire 64
## 837 2020-05-02 North East and Yorkshire 48
## 838 2020-05-03 North East and Yorkshire 40
## 839 2020-05-04 North East and Yorkshire 49
## 840 2020-05-05 North East and Yorkshire 40
## 841 2020-05-06 North East and Yorkshire 51
## 842 2020-05-07 North East and Yorkshire 45
## 843 2020-05-08 North East and Yorkshire 42
## 844 2020-05-09 North East and Yorkshire 44
## 845 2020-05-10 North East and Yorkshire 40
## 846 2020-05-11 North East and Yorkshire 29
## 847 2020-05-12 North East and Yorkshire 27
## 848 2020-05-13 North East and Yorkshire 28
## 849 2020-05-14 North East and Yorkshire 31
## 850 2020-05-15 North East and Yorkshire 32
## 851 2020-05-16 North East and Yorkshire 35
## 852 2020-05-17 North East and Yorkshire 26
## 853 2020-05-18 North East and Yorkshire 30
## 854 2020-05-19 North East and Yorkshire 27
## 855 2020-05-20 North East and Yorkshire 22
## 856 2020-05-21 North East and Yorkshire 33
## 857 2020-05-22 North East and Yorkshire 22
## 858 2020-05-23 North East and Yorkshire 18
## 859 2020-05-24 North East and Yorkshire 26
## 860 2020-05-25 North East and Yorkshire 21
## 861 2020-05-26 North East and Yorkshire 21
## 862 2020-05-27 North East and Yorkshire 22
## 863 2020-05-28 North East and Yorkshire 21
## 864 2020-05-29 North East and Yorkshire 25
## 865 2020-05-30 North East and Yorkshire 20
## 866 2020-05-31 North East and Yorkshire 20
## 867 2020-06-01 North East and Yorkshire 17
## 868 2020-06-02 North East and Yorkshire 23
## 869 2020-06-03 North East and Yorkshire 23
## 870 2020-06-04 North East and Yorkshire 17
## 871 2020-06-05 North East and Yorkshire 18
## 872 2020-06-06 North East and Yorkshire 21
## 873 2020-06-07 North East and Yorkshire 14
## 874 2020-06-08 North East and Yorkshire 11
## 875 2020-06-09 North East and Yorkshire 12
## 876 2020-06-10 North East and Yorkshire 19
## 877 2020-06-11 North East and Yorkshire 7
## 878 2020-06-12 North East and Yorkshire 9
## 879 2020-06-13 North East and Yorkshire 10
## 880 2020-06-14 North East and Yorkshire 11
## 881 2020-06-15 North East and Yorkshire 9
## 882 2020-06-16 North East and Yorkshire 10
## 883 2020-06-17 North East and Yorkshire 9
## 884 2020-06-18 North East and Yorkshire 11
## 885 2020-06-19 North East and Yorkshire 6
## 886 2020-06-20 North East and Yorkshire 5
## 887 2020-06-21 North East and Yorkshire 4
## 888 2020-06-22 North East and Yorkshire 7
## 889 2020-06-23 North East and Yorkshire 8
## 890 2020-06-24 North East and Yorkshire 10
## 891 2020-06-25 North East and Yorkshire 4
## 892 2020-06-26 North East and Yorkshire 8
## 893 2020-06-27 North East and Yorkshire 4
## 894 2020-06-28 North East and Yorkshire 5
## 895 2020-06-29 North East and Yorkshire 2
## 896 2020-06-30 North East and Yorkshire 7
## 897 2020-07-01 North East and Yorkshire 1
## 898 2020-07-02 North East and Yorkshire 4
## 899 2020-07-03 North East and Yorkshire 4
## 900 2020-07-04 North East and Yorkshire 4
## 901 2020-07-05 North East and Yorkshire 3
## 902 2020-07-06 North East and Yorkshire 2
## 903 2020-07-07 North East and Yorkshire 3
## 904 2020-07-08 North East and Yorkshire 3
## 905 2020-07-09 North East and Yorkshire 0
## 906 2020-07-10 North East and Yorkshire 3
## 907 2020-07-11 North East and Yorkshire 1
## 908 2020-07-12 North East and Yorkshire 4
## 909 2020-07-13 North East and Yorkshire 1
## 910 2020-07-14 North East and Yorkshire 1
## 911 2020-07-15 North East and Yorkshire 2
## 912 2020-07-16 North East and Yorkshire 3
## 913 2020-07-17 North East and Yorkshire 1
## 914 2020-07-18 North East and Yorkshire 2
## 915 2020-07-19 North East and Yorkshire 2
## 916 2020-07-20 North East and Yorkshire 1
## 917 2020-07-21 North East and Yorkshire 1
## 918 2020-07-22 North East and Yorkshire 6
## 919 2020-07-23 North East and Yorkshire 0
## 920 2020-07-24 North East and Yorkshire 1
## 921 2020-07-25 North East and Yorkshire 5
## 922 2020-07-26 North East and Yorkshire 1
## 923 2020-07-27 North East and Yorkshire 0
## 924 2020-07-28 North East and Yorkshire 2
## 925 2020-07-29 North East and Yorkshire 1
## 926 2020-07-30 North East and Yorkshire 0
## 927 2020-07-31 North East and Yorkshire 1
## 928 2020-08-01 North East and Yorkshire 3
## 929 2020-08-02 North East and Yorkshire 2
## 930 2020-08-03 North East and Yorkshire 1
## 931 2020-08-04 North East and Yorkshire 2
## 932 2020-08-05 North East and Yorkshire 1
## 933 2020-08-06 North East and Yorkshire 4
## 934 2020-08-07 North East and Yorkshire 0
## 935 2020-08-08 North East and Yorkshire 2
## 936 2020-08-09 North East and Yorkshire 3
## 937 2020-08-10 North East and Yorkshire 3
## 938 2020-08-11 North East and Yorkshire 2
## 939 2020-08-12 North East and Yorkshire 2
## 940 2020-08-13 North East and Yorkshire 0
## 941 2020-08-14 North East and Yorkshire 1
## 942 2020-08-15 North East and Yorkshire 1
## 943 2020-08-16 North East and Yorkshire 0
## 944 2020-08-17 North East and Yorkshire 6
## 945 2020-08-18 North East and Yorkshire 1
## 946 2020-08-19 North East and Yorkshire 0
## 947 2020-08-20 North East and Yorkshire 0
## 948 2020-08-21 North East and Yorkshire 1
## 949 2020-08-22 North East and Yorkshire 1
## 950 2020-08-23 North East and Yorkshire 3
## 951 2020-08-24 North East and Yorkshire 0
## 952 2020-08-25 North East and Yorkshire 1
## 953 2020-08-26 North East and Yorkshire 2
## 954 2020-08-27 North East and Yorkshire 1
## 955 2020-08-28 North East and Yorkshire 0
## 956 2020-08-29 North East and Yorkshire 1
## 957 2020-08-30 North East and Yorkshire 0
## 958 2020-08-31 North East and Yorkshire 0
## 959 2020-09-01 North East and Yorkshire 2
## 960 2020-09-02 North East and Yorkshire 3
## 961 2020-09-03 North East and Yorkshire 1
## 962 2020-09-04 North East and Yorkshire 1
## 963 2020-09-05 North East and Yorkshire 2
## 964 2020-09-06 North East and Yorkshire 1
## 965 2020-09-07 North East and Yorkshire 0
## 966 2020-09-08 North East and Yorkshire 1
## 967 2020-09-09 North East and Yorkshire 2
## 968 2020-09-10 North East and Yorkshire 0
## 969 2020-09-11 North East and Yorkshire 3
## 970 2020-09-12 North East and Yorkshire 1
## 971 2020-09-13 North East and Yorkshire 3
## 972 2020-09-14 North East and Yorkshire 4
## 973 2020-09-15 North East and Yorkshire 3
## 974 2020-09-16 North East and Yorkshire 3
## 975 2020-09-17 North East and Yorkshire 5
## 976 2020-09-18 North East and Yorkshire 6
## 977 2020-09-19 North East and Yorkshire 2
## 978 2020-09-20 North East and Yorkshire 9
## 979 2020-09-21 North East and Yorkshire 7
## 980 2020-09-22 North East and Yorkshire 4
## 981 2020-09-23 North East and Yorkshire 6
## 982 2020-09-24 North East and Yorkshire 3
## 983 2020-09-25 North East and Yorkshire 5
## 984 2020-09-26 North East and Yorkshire 7
## 985 2020-09-27 North East and Yorkshire 10
## 986 2020-09-28 North East and Yorkshire 6
## 987 2020-09-29 North East and Yorkshire 7
## 988 2020-09-30 North East and Yorkshire 7
## 989 2020-10-01 North East and Yorkshire 8
## 990 2020-10-02 North East and Yorkshire 16
## 991 2020-10-03 North East and Yorkshire 12
## 992 2020-10-04 North East and Yorkshire 13
## 993 2020-10-05 North East and Yorkshire 10
## 994 2020-10-06 North East and Yorkshire 15
## 995 2020-10-07 North East and Yorkshire 13
## 996 2020-10-08 North East and Yorkshire 16
## 997 2020-10-09 North East and Yorkshire 10
## 998 2020-10-10 North East and Yorkshire 16
## 999 2020-10-11 North East and Yorkshire 16
## 1000 2020-10-12 North East and Yorkshire 15
## 1001 2020-10-13 North East and Yorkshire 20
## 1002 2020-10-14 North East and Yorkshire 20
## 1003 2020-10-15 North East and Yorkshire 23
## 1004 2020-10-16 North East and Yorkshire 24
## 1005 2020-10-17 North East and Yorkshire 32
## 1006 2020-10-18 North East and Yorkshire 21
## 1007 2020-10-19 North East and Yorkshire 31
## 1008 2020-10-20 North East and Yorkshire 35
## 1009 2020-10-21 North East and Yorkshire 42
## 1010 2020-10-22 North East and Yorkshire 33
## 1011 2020-10-23 North East and Yorkshire 30
## 1012 2020-10-24 North East and Yorkshire 33
## 1013 2020-10-25 North East and Yorkshire 33
## 1014 2020-10-26 North East and Yorkshire 43
## 1015 2020-10-27 North East and Yorkshire 44
## 1016 2020-10-28 North East and Yorkshire 38
## 1017 2020-10-29 North East and Yorkshire 47
## 1018 2020-10-30 North East and Yorkshire 44
## 1019 2020-10-31 North East and Yorkshire 54
## 1020 2020-11-01 North East and Yorkshire 46
## 1021 2020-11-02 North East and Yorkshire 49
## 1022 2020-11-03 North East and Yorkshire 45
## 1023 2020-11-04 North East and Yorkshire 56
## 1024 2020-11-05 North East and Yorkshire 50
## 1025 2020-11-06 North East and Yorkshire 54
## 1026 2020-11-07 North East and Yorkshire 72
## 1027 2020-11-08 North East and Yorkshire 55
## 1028 2020-11-09 North East and Yorkshire 80
## 1029 2020-11-10 North East and Yorkshire 54
## 1030 2020-11-11 North East and Yorkshire 55
## 1031 2020-11-12 North East and Yorkshire 58
## 1032 2020-11-13 North East and Yorkshire 16
## 1033 2020-03-01 North West 0
## 1034 2020-03-02 North West 0
## 1035 2020-03-03 North West 0
## 1036 2020-03-04 North West 0
## 1037 2020-03-05 North West 1
## 1038 2020-03-06 North West 0
## 1039 2020-03-07 North West 0
## 1040 2020-03-08 North West 1
## 1041 2020-03-09 North West 0
## 1042 2020-03-10 North West 0
## 1043 2020-03-11 North West 0
## 1044 2020-03-12 North West 2
## 1045 2020-03-13 North West 3
## 1046 2020-03-14 North West 1
## 1047 2020-03-15 North West 4
## 1048 2020-03-16 North West 2
## 1049 2020-03-17 North West 4
## 1050 2020-03-18 North West 6
## 1051 2020-03-19 North West 7
## 1052 2020-03-20 North West 10
## 1053 2020-03-21 North West 11
## 1054 2020-03-22 North West 13
## 1055 2020-03-23 North West 15
## 1056 2020-03-24 North West 21
## 1057 2020-03-25 North West 21
## 1058 2020-03-26 North West 29
## 1059 2020-03-27 North West 36
## 1060 2020-03-28 North West 28
## 1061 2020-03-29 North West 46
## 1062 2020-03-30 North West 67
## 1063 2020-03-31 North West 52
## 1064 2020-04-01 North West 86
## 1065 2020-04-02 North West 96
## 1066 2020-04-03 North West 95
## 1067 2020-04-04 North West 98
## 1068 2020-04-05 North West 102
## 1069 2020-04-06 North West 100
## 1070 2020-04-07 North West 135
## 1071 2020-04-08 North West 127
## 1072 2020-04-09 North West 119
## 1073 2020-04-10 North West 117
## 1074 2020-04-11 North West 138
## 1075 2020-04-12 North West 125
## 1076 2020-04-13 North West 129
## 1077 2020-04-14 North West 130
## 1078 2020-04-15 North West 114
## 1079 2020-04-16 North West 135
## 1080 2020-04-17 North West 98
## 1081 2020-04-18 North West 113
## 1082 2020-04-19 North West 71
## 1083 2020-04-20 North West 83
## 1084 2020-04-21 North West 76
## 1085 2020-04-22 North West 86
## 1086 2020-04-23 North West 85
## 1087 2020-04-24 North West 66
## 1088 2020-04-25 North West 66
## 1089 2020-04-26 North West 55
## 1090 2020-04-27 North West 54
## 1091 2020-04-28 North West 57
## 1092 2020-04-29 North West 63
## 1093 2020-04-30 North West 60
## 1094 2020-05-01 North West 45
## 1095 2020-05-02 North West 56
## 1096 2020-05-03 North West 55
## 1097 2020-05-04 North West 48
## 1098 2020-05-05 North West 48
## 1099 2020-05-06 North West 44
## 1100 2020-05-07 North West 49
## 1101 2020-05-08 North West 42
## 1102 2020-05-09 North West 31
## 1103 2020-05-10 North West 42
## 1104 2020-05-11 North West 35
## 1105 2020-05-12 North West 38
## 1106 2020-05-13 North West 25
## 1107 2020-05-14 North West 26
## 1108 2020-05-15 North West 33
## 1109 2020-05-16 North West 32
## 1110 2020-05-17 North West 24
## 1111 2020-05-18 North West 31
## 1112 2020-05-19 North West 35
## 1113 2020-05-20 North West 27
## 1114 2020-05-21 North West 28
## 1115 2020-05-22 North West 26
## 1116 2020-05-23 North West 31
## 1117 2020-05-24 North West 26
## 1118 2020-05-25 North West 31
## 1119 2020-05-26 North West 27
## 1120 2020-05-27 North West 27
## 1121 2020-05-28 North West 28
## 1122 2020-05-29 North West 20
## 1123 2020-05-30 North West 19
## 1124 2020-05-31 North West 13
## 1125 2020-06-01 North West 12
## 1126 2020-06-02 North West 27
## 1127 2020-06-03 North West 22
## 1128 2020-06-04 North West 22
## 1129 2020-06-05 North West 16
## 1130 2020-06-06 North West 26
## 1131 2020-06-07 North West 20
## 1132 2020-06-08 North West 23
## 1133 2020-06-09 North West 17
## 1134 2020-06-10 North West 16
## 1135 2020-06-11 North West 16
## 1136 2020-06-12 North West 11
## 1137 2020-06-13 North West 10
## 1138 2020-06-14 North West 15
## 1139 2020-06-15 North West 16
## 1140 2020-06-16 North West 16
## 1141 2020-06-17 North West 13
## 1142 2020-06-18 North West 14
## 1143 2020-06-19 North West 7
## 1144 2020-06-20 North West 11
## 1145 2020-06-21 North West 8
## 1146 2020-06-22 North West 11
## 1147 2020-06-23 North West 13
## 1148 2020-06-24 North West 13
## 1149 2020-06-25 North West 15
## 1150 2020-06-26 North West 6
## 1151 2020-06-27 North West 7
## 1152 2020-06-28 North West 9
## 1153 2020-06-29 North West 9
## 1154 2020-06-30 North West 7
## 1155 2020-07-01 North West 3
## 1156 2020-07-02 North West 6
## 1157 2020-07-03 North West 7
## 1158 2020-07-04 North West 4
## 1159 2020-07-05 North West 6
## 1160 2020-07-06 North West 9
## 1161 2020-07-07 North West 8
## 1162 2020-07-08 North West 5
## 1163 2020-07-09 North West 10
## 1164 2020-07-10 North West 2
## 1165 2020-07-11 North West 5
## 1166 2020-07-12 North West 0
## 1167 2020-07-13 North West 6
## 1168 2020-07-14 North West 4
## 1169 2020-07-15 North West 5
## 1170 2020-07-16 North West 2
## 1171 2020-07-17 North West 4
## 1172 2020-07-18 North West 5
## 1173 2020-07-19 North West 3
## 1174 2020-07-20 North West 0
## 1175 2020-07-21 North West 2
## 1176 2020-07-22 North West 3
## 1177 2020-07-23 North West 3
## 1178 2020-07-24 North West 1
## 1179 2020-07-25 North West 1
## 1180 2020-07-26 North West 3
## 1181 2020-07-27 North West 1
## 1182 2020-07-28 North West 1
## 1183 2020-07-29 North West 2
## 1184 2020-07-30 North West 2
## 1185 2020-07-31 North West 0
## 1186 2020-08-01 North West 2
## 1187 2020-08-02 North West 1
## 1188 2020-08-03 North West 8
## 1189 2020-08-04 North West 3
## 1190 2020-08-05 North West 2
## 1191 2020-08-06 North West 2
## 1192 2020-08-07 North West 2
## 1193 2020-08-08 North West 2
## 1194 2020-08-09 North West 3
## 1195 2020-08-10 North West 2
## 1196 2020-08-11 North West 3
## 1197 2020-08-12 North West 0
## 1198 2020-08-13 North West 2
## 1199 2020-08-14 North West 2
## 1200 2020-08-15 North West 6
## 1201 2020-08-16 North West 2
## 1202 2020-08-17 North West 1
## 1203 2020-08-18 North West 2
## 1204 2020-08-19 North West 1
## 1205 2020-08-20 North West 1
## 1206 2020-08-21 North West 4
## 1207 2020-08-22 North West 3
## 1208 2020-08-23 North West 5
## 1209 2020-08-24 North West 4
## 1210 2020-08-25 North West 3
## 1211 2020-08-26 North West 4
## 1212 2020-08-27 North West 1
## 1213 2020-08-28 North West 2
## 1214 2020-08-29 North West 0
## 1215 2020-08-30 North West 2
## 1216 2020-08-31 North West 3
## 1217 2020-09-01 North West 0
## 1218 2020-09-02 North West 2
## 1219 2020-09-03 North West 1
## 1220 2020-09-04 North West 3
## 1221 2020-09-05 North West 6
## 1222 2020-09-06 North West 1
## 1223 2020-09-07 North West 8
## 1224 2020-09-08 North West 6
## 1225 2020-09-09 North West 5
## 1226 2020-09-10 North West 4
## 1227 2020-09-11 North West 0
## 1228 2020-09-12 North West 4
## 1229 2020-09-13 North West 2
## 1230 2020-09-14 North West 4
## 1231 2020-09-15 North West 4
## 1232 2020-09-16 North West 6
## 1233 2020-09-17 North West 7
## 1234 2020-09-18 North West 6
## 1235 2020-09-19 North West 3
## 1236 2020-09-20 North West 2
## 1237 2020-09-21 North West 2
## 1238 2020-09-22 North West 9
## 1239 2020-09-23 North West 14
## 1240 2020-09-24 North West 10
## 1241 2020-09-25 North West 8
## 1242 2020-09-26 North West 14
## 1243 2020-09-27 North West 11
## 1244 2020-09-28 North West 15
## 1245 2020-09-29 North West 12
## 1246 2020-09-30 North West 17
## 1247 2020-10-01 North West 17
## 1248 2020-10-02 North West 20
## 1249 2020-10-03 North West 15
## 1250 2020-10-04 North West 15
## 1251 2020-10-05 North West 15
## 1252 2020-10-06 North West 20
## 1253 2020-10-07 North West 20
## 1254 2020-10-08 North West 22
## 1255 2020-10-09 North West 23
## 1256 2020-10-10 North West 31
## 1257 2020-10-11 North West 31
## 1258 2020-10-12 North West 35
## 1259 2020-10-13 North West 26
## 1260 2020-10-14 North West 35
## 1261 2020-10-15 North West 36
## 1262 2020-10-16 North West 34
## 1263 2020-10-17 North West 52
## 1264 2020-10-18 North West 40
## 1265 2020-10-19 North West 43
## 1266 2020-10-20 North West 48
## 1267 2020-10-21 North West 51
## 1268 2020-10-22 North West 49
## 1269 2020-10-23 North West 49
## 1270 2020-10-24 North West 50
## 1271 2020-10-25 North West 62
## 1272 2020-10-26 North West 53
## 1273 2020-10-27 North West 48
## 1274 2020-10-28 North West 57
## 1275 2020-10-29 North West 71
## 1276 2020-10-30 North West 68
## 1277 2020-10-31 North West 62
## 1278 2020-11-01 North West 72
## 1279 2020-11-02 North West 62
## 1280 2020-11-03 North West 76
## 1281 2020-11-04 North West 64
## 1282 2020-11-05 North West 66
## 1283 2020-11-06 North West 71
## 1284 2020-11-07 North West 73
## 1285 2020-11-08 North West 81
## 1286 2020-11-09 North West 77
## 1287 2020-11-10 North West 59
## 1288 2020-11-11 North West 46
## 1289 2020-11-12 North West 53
## 1290 2020-11-13 North West 27
## 1291 2020-03-01 South East 0
## 1292 2020-03-02 South East 0
## 1293 2020-03-03 South East 1
## 1294 2020-03-04 South East 0
## 1295 2020-03-05 South East 1
## 1296 2020-03-06 South East 0
## 1297 2020-03-07 South East 0
## 1298 2020-03-08 South East 1
## 1299 2020-03-09 South East 1
## 1300 2020-03-10 South East 1
## 1301 2020-03-11 South East 1
## 1302 2020-03-12 South East 0
## 1303 2020-03-13 South East 1
## 1304 2020-03-14 South East 1
## 1305 2020-03-15 South East 5
## 1306 2020-03-16 South East 8
## 1307 2020-03-17 South East 7
## 1308 2020-03-18 South East 10
## 1309 2020-03-19 South East 9
## 1310 2020-03-20 South East 13
## 1311 2020-03-21 South East 7
## 1312 2020-03-22 South East 25
## 1313 2020-03-23 South East 20
## 1314 2020-03-24 South East 22
## 1315 2020-03-25 South East 29
## 1316 2020-03-26 South East 35
## 1317 2020-03-27 South East 36
## 1318 2020-03-28 South East 36
## 1319 2020-03-29 South East 55
## 1320 2020-03-30 South East 58
## 1321 2020-03-31 South East 65
## 1322 2020-04-01 South East 66
## 1323 2020-04-02 South East 55
## 1324 2020-04-03 South East 72
## 1325 2020-04-04 South East 80
## 1326 2020-04-05 South East 82
## 1327 2020-04-06 South East 88
## 1328 2020-04-07 South East 100
## 1329 2020-04-08 South East 83
## 1330 2020-04-09 South East 104
## 1331 2020-04-10 South East 88
## 1332 2020-04-11 South East 88
## 1333 2020-04-12 South East 88
## 1334 2020-04-13 South East 84
## 1335 2020-04-14 South East 65
## 1336 2020-04-15 South East 72
## 1337 2020-04-16 South East 56
## 1338 2020-04-17 South East 86
## 1339 2020-04-18 South East 57
## 1340 2020-04-19 South East 70
## 1341 2020-04-20 South East 87
## 1342 2020-04-21 South East 51
## 1343 2020-04-22 South East 54
## 1344 2020-04-23 South East 57
## 1345 2020-04-24 South East 64
## 1346 2020-04-25 South East 51
## 1347 2020-04-26 South East 51
## 1348 2020-04-27 South East 41
## 1349 2020-04-28 South East 40
## 1350 2020-04-29 South East 47
## 1351 2020-04-30 South East 29
## 1352 2020-05-01 South East 37
## 1353 2020-05-02 South East 36
## 1354 2020-05-03 South East 17
## 1355 2020-05-04 South East 35
## 1356 2020-05-05 South East 29
## 1357 2020-05-06 South East 25
## 1358 2020-05-07 South East 27
## 1359 2020-05-08 South East 26
## 1360 2020-05-09 South East 28
## 1361 2020-05-10 South East 19
## 1362 2020-05-11 South East 25
## 1363 2020-05-12 South East 27
## 1364 2020-05-13 South East 18
## 1365 2020-05-14 South East 32
## 1366 2020-05-15 South East 25
## 1367 2020-05-16 South East 22
## 1368 2020-05-17 South East 18
## 1369 2020-05-18 South East 22
## 1370 2020-05-19 South East 12
## 1371 2020-05-20 South East 22
## 1372 2020-05-21 South East 15
## 1373 2020-05-22 South East 17
## 1374 2020-05-23 South East 21
## 1375 2020-05-24 South East 17
## 1376 2020-05-25 South East 13
## 1377 2020-05-26 South East 19
## 1378 2020-05-27 South East 19
## 1379 2020-05-28 South East 12
## 1380 2020-05-29 South East 22
## 1381 2020-05-30 South East 8
## 1382 2020-05-31 South East 12
## 1383 2020-06-01 South East 11
## 1384 2020-06-02 South East 13
## 1385 2020-06-03 South East 18
## 1386 2020-06-04 South East 11
## 1387 2020-06-05 South East 11
## 1388 2020-06-06 South East 10
## 1389 2020-06-07 South East 12
## 1390 2020-06-08 South East 8
## 1391 2020-06-09 South East 10
## 1392 2020-06-10 South East 11
## 1393 2020-06-11 South East 5
## 1394 2020-06-12 South East 6
## 1395 2020-06-13 South East 7
## 1396 2020-06-14 South East 7
## 1397 2020-06-15 South East 8
## 1398 2020-06-16 South East 14
## 1399 2020-06-17 South East 9
## 1400 2020-06-18 South East 4
## 1401 2020-06-19 South East 7
## 1402 2020-06-20 South East 5
## 1403 2020-06-21 South East 3
## 1404 2020-06-22 South East 2
## 1405 2020-06-23 South East 9
## 1406 2020-06-24 South East 7
## 1407 2020-06-25 South East 5
## 1408 2020-06-26 South East 8
## 1409 2020-06-27 South East 9
## 1410 2020-06-28 South East 6
## 1411 2020-06-29 South East 5
## 1412 2020-06-30 South East 5
## 1413 2020-07-01 South East 2
## 1414 2020-07-02 South East 8
## 1415 2020-07-03 South East 3
## 1416 2020-07-04 South East 6
## 1417 2020-07-05 South East 5
## 1418 2020-07-06 South East 4
## 1419 2020-07-07 South East 6
## 1420 2020-07-08 South East 3
## 1421 2020-07-09 South East 7
## 1422 2020-07-10 South East 3
## 1423 2020-07-11 South East 4
## 1424 2020-07-12 South East 4
## 1425 2020-07-13 South East 5
## 1426 2020-07-14 South East 5
## 1427 2020-07-15 South East 6
## 1428 2020-07-16 South East 3
## 1429 2020-07-17 South East 1
## 1430 2020-07-18 South East 5
## 1431 2020-07-19 South East 2
## 1432 2020-07-20 South East 6
## 1433 2020-07-21 South East 4
## 1434 2020-07-22 South East 2
## 1435 2020-07-23 South East 3
## 1436 2020-07-24 South East 1
## 1437 2020-07-25 South East 1
## 1438 2020-07-26 South East 3
## 1439 2020-07-27 South East 1
## 1440 2020-07-28 South East 3
## 1441 2020-07-29 South East 2
## 1442 2020-07-30 South East 3
## 1443 2020-07-31 South East 1
## 1444 2020-08-01 South East 2
## 1445 2020-08-02 South East 4
## 1446 2020-08-03 South East 0
## 1447 2020-08-04 South East 0
## 1448 2020-08-05 South East 0
## 1449 2020-08-06 South East 2
## 1450 2020-08-07 South East 0
## 1451 2020-08-08 South East 2
## 1452 2020-08-09 South East 0
## 1453 2020-08-10 South East 2
## 1454 2020-08-11 South East 1
## 1455 2020-08-12 South East 1
## 1456 2020-08-13 South East 0
## 1457 2020-08-14 South East 0
## 1458 2020-08-15 South East 2
## 1459 2020-08-16 South East 1
## 1460 2020-08-17 South East 0
## 1461 2020-08-18 South East 2
## 1462 2020-08-19 South East 1
## 1463 2020-08-20 South East 0
## 1464 2020-08-21 South East 0
## 1465 2020-08-22 South East 0
## 1466 2020-08-23 South East 1
## 1467 2020-08-24 South East 0
## 1468 2020-08-25 South East 1
## 1469 2020-08-26 South East 0
## 1470 2020-08-27 South East 1
## 1471 2020-08-28 South East 2
## 1472 2020-08-29 South East 1
## 1473 2020-08-30 South East 0
## 1474 2020-08-31 South East 2
## 1475 2020-09-01 South East 1
## 1476 2020-09-02 South East 1
## 1477 2020-09-03 South East 0
## 1478 2020-09-04 South East 1
## 1479 2020-09-05 South East 0
## 1480 2020-09-06 South East 1
## 1481 2020-09-07 South East 0
## 1482 2020-09-08 South East 0
## 1483 2020-09-09 South East 0
## 1484 2020-09-10 South East 1
## 1485 2020-09-11 South East 1
## 1486 2020-09-12 South East 0
## 1487 2020-09-13 South East 3
## 1488 2020-09-14 South East 1
## 1489 2020-09-15 South East 2
## 1490 2020-09-16 South East 2
## 1491 2020-09-17 South East 3
## 1492 2020-09-18 South East 1
## 1493 2020-09-19 South East 1
## 1494 2020-09-20 South East 0
## 1495 2020-09-21 South East 3
## 1496 2020-09-22 South East 0
## 1497 2020-09-23 South East 2
## 1498 2020-09-24 South East 0
## 1499 2020-09-25 South East 3
## 1500 2020-09-26 South East 2
## 1501 2020-09-27 South East 2
## 1502 2020-09-28 South East 6
## 1503 2020-09-29 South East 3
## 1504 2020-09-30 South East 4
## 1505 2020-10-01 South East 4
## 1506 2020-10-02 South East 2
## 1507 2020-10-03 South East 1
## 1508 2020-10-04 South East 1
## 1509 2020-10-05 South East 2
## 1510 2020-10-06 South East 1
## 1511 2020-10-07 South East 4
## 1512 2020-10-08 South East 1
## 1513 2020-10-09 South East 1
## 1514 2020-10-10 South East 3
## 1515 2020-10-11 South East 3
## 1516 2020-10-12 South East 4
## 1517 2020-10-13 South East 2
## 1518 2020-10-14 South East 2
## 1519 2020-10-15 South East 3
## 1520 2020-10-16 South East 2
## 1521 2020-10-17 South East 3
## 1522 2020-10-18 South East 4
## 1523 2020-10-19 South East 6
## 1524 2020-10-20 South East 8
## 1525 2020-10-21 South East 9
## 1526 2020-10-22 South East 5
## 1527 2020-10-23 South East 7
## 1528 2020-10-24 South East 5
## 1529 2020-10-25 South East 9
## 1530 2020-10-26 South East 13
## 1531 2020-10-27 South East 10
## 1532 2020-10-28 South East 10
## 1533 2020-10-29 South East 7
## 1534 2020-10-30 South East 6
## 1535 2020-10-31 South East 15
## 1536 2020-11-01 South East 18
## 1537 2020-11-02 South East 13
## 1538 2020-11-03 South East 16
## 1539 2020-11-04 South East 10
## 1540 2020-11-05 South East 10
## 1541 2020-11-06 South East 15
## 1542 2020-11-07 South East 15
## 1543 2020-11-08 South East 16
## 1544 2020-11-09 South East 18
## 1545 2020-11-10 South East 17
## 1546 2020-11-11 South East 16
## 1547 2020-11-12 South East 16
## 1548 2020-11-13 South East 1
## 1549 2020-03-01 South West 0
## 1550 2020-03-02 South West 0
## 1551 2020-03-03 South West 0
## 1552 2020-03-04 South West 0
## 1553 2020-03-05 South West 0
## 1554 2020-03-06 South West 0
## 1555 2020-03-07 South West 0
## 1556 2020-03-08 South West 0
## 1557 2020-03-09 South West 0
## 1558 2020-03-10 South West 0
## 1559 2020-03-11 South West 1
## 1560 2020-03-12 South West 0
## 1561 2020-03-13 South West 0
## 1562 2020-03-14 South West 1
## 1563 2020-03-15 South West 0
## 1564 2020-03-16 South West 0
## 1565 2020-03-17 South West 2
## 1566 2020-03-18 South West 2
## 1567 2020-03-19 South West 4
## 1568 2020-03-20 South West 3
## 1569 2020-03-21 South West 6
## 1570 2020-03-22 South West 7
## 1571 2020-03-23 South West 8
## 1572 2020-03-24 South West 7
## 1573 2020-03-25 South West 9
## 1574 2020-03-26 South West 11
## 1575 2020-03-27 South West 13
## 1576 2020-03-28 South West 21
## 1577 2020-03-29 South West 18
## 1578 2020-03-30 South West 23
## 1579 2020-03-31 South West 23
## 1580 2020-04-01 South West 21
## 1581 2020-04-02 South West 23
## 1582 2020-04-03 South West 30
## 1583 2020-04-04 South West 42
## 1584 2020-04-05 South West 32
## 1585 2020-04-06 South West 34
## 1586 2020-04-07 South West 39
## 1587 2020-04-08 South West 47
## 1588 2020-04-09 South West 24
## 1589 2020-04-10 South West 46
## 1590 2020-04-11 South West 43
## 1591 2020-04-12 South West 23
## 1592 2020-04-13 South West 27
## 1593 2020-04-14 South West 24
## 1594 2020-04-15 South West 32
## 1595 2020-04-16 South West 29
## 1596 2020-04-17 South West 33
## 1597 2020-04-18 South West 25
## 1598 2020-04-19 South West 31
## 1599 2020-04-20 South West 26
## 1600 2020-04-21 South West 26
## 1601 2020-04-22 South West 23
## 1602 2020-04-23 South West 17
## 1603 2020-04-24 South West 19
## 1604 2020-04-25 South West 15
## 1605 2020-04-26 South West 27
## 1606 2020-04-27 South West 13
## 1607 2020-04-28 South West 17
## 1608 2020-04-29 South West 15
## 1609 2020-04-30 South West 26
## 1610 2020-05-01 South West 6
## 1611 2020-05-02 South West 7
## 1612 2020-05-03 South West 10
## 1613 2020-05-04 South West 17
## 1614 2020-05-05 South West 14
## 1615 2020-05-06 South West 19
## 1616 2020-05-07 South West 16
## 1617 2020-05-08 South West 6
## 1618 2020-05-09 South West 11
## 1619 2020-05-10 South West 5
## 1620 2020-05-11 South West 8
## 1621 2020-05-12 South West 7
## 1622 2020-05-13 South West 7
## 1623 2020-05-14 South West 6
## 1624 2020-05-15 South West 4
## 1625 2020-05-16 South West 4
## 1626 2020-05-17 South West 6
## 1627 2020-05-18 South West 4
## 1628 2020-05-19 South West 6
## 1629 2020-05-20 South West 1
## 1630 2020-05-21 South West 9
## 1631 2020-05-22 South West 7
## 1632 2020-05-23 South West 6
## 1633 2020-05-24 South West 3
## 1634 2020-05-25 South West 8
## 1635 2020-05-26 South West 11
## 1636 2020-05-27 South West 5
## 1637 2020-05-28 South West 10
## 1638 2020-05-29 South West 7
## 1639 2020-05-30 South West 3
## 1640 2020-05-31 South West 2
## 1641 2020-06-01 South West 7
## 1642 2020-06-02 South West 2
## 1643 2020-06-03 South West 7
## 1644 2020-06-04 South West 2
## 1645 2020-06-05 South West 2
## 1646 2020-06-06 South West 1
## 1647 2020-06-07 South West 3
## 1648 2020-06-08 South West 3
## 1649 2020-06-09 South West 0
## 1650 2020-06-10 South West 1
## 1651 2020-06-11 South West 2
## 1652 2020-06-12 South West 2
## 1653 2020-06-13 South West 2
## 1654 2020-06-14 South West 0
## 1655 2020-06-15 South West 2
## 1656 2020-06-16 South West 2
## 1657 2020-06-17 South West 0
## 1658 2020-06-18 South West 0
## 1659 2020-06-19 South West 0
## 1660 2020-06-20 South West 2
## 1661 2020-06-21 South West 0
## 1662 2020-06-22 South West 1
## 1663 2020-06-23 South West 1
## 1664 2020-06-24 South West 1
## 1665 2020-06-25 South West 0
## 1666 2020-06-26 South West 3
## 1667 2020-06-27 South West 0
## 1668 2020-06-28 South West 0
## 1669 2020-06-29 South West 1
## 1670 2020-06-30 South West 0
## 1671 2020-07-01 South West 0
## 1672 2020-07-02 South West 0
## 1673 2020-07-03 South West 0
## 1674 2020-07-04 South West 0
## 1675 2020-07-05 South West 1
## 1676 2020-07-06 South West 0
## 1677 2020-07-07 South West 0
## 1678 2020-07-08 South West 2
## 1679 2020-07-09 South West 0
## 1680 2020-07-10 South West 1
## 1681 2020-07-11 South West 0
## 1682 2020-07-12 South West 0
## 1683 2020-07-13 South West 1
## 1684 2020-07-14 South West 0
## 1685 2020-07-15 South West 0
## 1686 2020-07-16 South West 0
## 1687 2020-07-17 South West 1
## 1688 2020-07-18 South West 0
## 1689 2020-07-19 South West 0
## 1690 2020-07-20 South West 0
## 1691 2020-07-21 South West 0
## 1692 2020-07-22 South West 0
## 1693 2020-07-23 South West 0
## 1694 2020-07-24 South West 0
## 1695 2020-07-25 South West 0
## 1696 2020-07-26 South West 0
## 1697 2020-07-27 South West 0
## 1698 2020-07-28 South West 0
## 1699 2020-07-29 South West 0
## 1700 2020-07-30 South West 1
## 1701 2020-07-31 South West 0
## 1702 2020-08-01 South West 0
## 1703 2020-08-02 South West 0
## 1704 2020-08-03 South West 0
## 1705 2020-08-04 South West 0
## 1706 2020-08-05 South West 0
## 1707 2020-08-06 South West 0
## 1708 2020-08-07 South West 0
## 1709 2020-08-08 South West 0
## 1710 2020-08-09 South West 0
## 1711 2020-08-10 South West 0
## 1712 2020-08-11 South West 0
## 1713 2020-08-12 South West 0
## 1714 2020-08-13 South West 0
## 1715 2020-08-14 South West 1
## 1716 2020-08-15 South West 0
## 1717 2020-08-16 South West 0
## 1718 2020-08-17 South West 2
## 1719 2020-08-18 South West 0
## 1720 2020-08-19 South West 0
## 1721 2020-08-20 South West 0
## 1722 2020-08-21 South West 0
## 1723 2020-08-22 South West 0
## 1724 2020-08-23 South West 0
## 1725 2020-08-24 South West 0
## 1726 2020-08-25 South West 1
## 1727 2020-08-26 South West 0
## 1728 2020-08-27 South West 1
## 1729 2020-08-28 South West 0
## 1730 2020-08-29 South West 0
## 1731 2020-08-30 South West 0
## 1732 2020-08-31 South West 0
## 1733 2020-09-01 South West 0
## 1734 2020-09-02 South West 0
## 1735 2020-09-03 South West 0
## 1736 2020-09-04 South West 0
## 1737 2020-09-05 South West 0
## 1738 2020-09-06 South West 0
## 1739 2020-09-07 South West 0
## 1740 2020-09-08 South West 1
## 1741 2020-09-09 South West 0
## 1742 2020-09-10 South West 0
## 1743 2020-09-11 South West 0
## 1744 2020-09-12 South West 0
## 1745 2020-09-13 South West 1
## 1746 2020-09-14 South West 0
## 1747 2020-09-15 South West 0
## 1748 2020-09-16 South West 0
## 1749 2020-09-17 South West 1
## 1750 2020-09-18 South West 0
## 1751 2020-09-19 South West 0
## 1752 2020-09-20 South West 1
## 1753 2020-09-21 South West 0
## 1754 2020-09-22 South West 0
## 1755 2020-09-23 South West 0
## 1756 2020-09-24 South West 1
## 1757 2020-09-25 South West 0
## 1758 2020-09-26 South West 0
## 1759 2020-09-27 South West 0
## 1760 2020-09-28 South West 0
## 1761 2020-09-29 South West 0
## 1762 2020-09-30 South West 0
## 1763 2020-10-01 South West 0
## 1764 2020-10-02 South West 1
## 1765 2020-10-03 South West 0
## 1766 2020-10-04 South West 0
## 1767 2020-10-05 South West 0
## 1768 2020-10-06 South West 1
## 1769 2020-10-07 South West 0
## 1770 2020-10-08 South West 1
## 1771 2020-10-09 South West 1
## 1772 2020-10-10 South West 0
## 1773 2020-10-11 South West 4
## 1774 2020-10-12 South West 2
## 1775 2020-10-13 South West 0
## 1776 2020-10-14 South West 3
## 1777 2020-10-15 South West 1
## 1778 2020-10-16 South West 2
## 1779 2020-10-17 South West 8
## 1780 2020-10-18 South West 2
## 1781 2020-10-19 South West 2
## 1782 2020-10-20 South West 3
## 1783 2020-10-21 South West 6
## 1784 2020-10-22 South West 6
## 1785 2020-10-23 South West 5
## 1786 2020-10-24 South West 5
## 1787 2020-10-25 South West 5
## 1788 2020-10-26 South West 7
## 1789 2020-10-27 South West 6
## 1790 2020-10-28 South West 7
## 1791 2020-10-29 South West 10
## 1792 2020-10-30 South West 8
## 1793 2020-10-31 South West 4
## 1794 2020-11-01 South West 5
## 1795 2020-11-02 South West 11
## 1796 2020-11-03 South West 6
## 1797 2020-11-04 South West 8
## 1798 2020-11-05 South West 5
## 1799 2020-11-06 South West 11
## 1800 2020-11-07 South West 9
## 1801 2020-11-08 South West 10
## 1802 2020-11-09 South West 11
## 1803 2020-11-10 South West 5
## 1804 2020-11-11 South West 12
## 1805 2020-11-12 South West 13
## 1806 2020-11-13 South West 2We extract the completion date from the NHS Pathways file timestamp:
The completion date of the NHS Pathways data is Thursday 12 Nov 2020.
These are functions which will be used further in the analyses.
Function to estimate the generalised R-squared as the proportion of deviance explained by a given model:
## Function to calculate R2 for Poisson model
## not adjusted for model complexity but all models have the same DF here
Rsq <- function(x) {
1 - (x$deviance / x$null.deviance)
}Function to extract growth rates per region as well as halving times, and the associated 95% confidence intervals:
## function to extract the coefficients, find the level of the intercept,
## reconstruct the values of r, get confidence intervals
get_r <- function(model) {
## extract coefficients and conf int
out <- data.frame(r = coef(model)) %>%
rownames_to_column("var") %>%
cbind(confint(model)) %>%
filter(!grepl("day_of_week", var)) %>%
filter(grepl("day", var)) %>%
rename(lower_95 = "2.5 %",
upper_95 = "97.5 %") %>%
mutate(var = sub("day:", "", var))
## reconstruct values: intercept + region-coefficient
for (i in 2:nrow(out)) {
out[i, -1] <- out[1, -1] + out[i, -1]
}
## find the name of the intercept, restore regions names
out <- out %>%
mutate(nhs_region = model$xlevels$nhs_region) %>%
select(nhs_region, everything(), -var)
## find halving times
halving <- log(0.5) / out[,-1] %>%
rename(halving_t = r,
halving_t_lower_95 = lower_95,
halving_t_upper_95 = upper_95)
## set halving times with exclusion intervals to NA
no_halving <- out$lower_95 < 0 & out$upper_95 > 0
halving[no_halving, ] <- NA_real_
## return all data
cbind(out, halving)
}Functions used in the correlation analysis between NHS Pathways reports and deaths:
## Function to calculate Pearson's correlation between deaths and lagged
## reports. Note that `pearson` can be replaced with `spearman` for rank
## correlation.
getcor <- function(x, ndx) {
return(cor(x$deaths[ndx],
x$note_lag[ndx],
use = "complete.obs",
method = "pearson"))
}
## Catch if sample size throws an error
getcor2 <- possibly(getcor, otherwise = NA)
getboot <- function(x) {
result <- boot::boot.ci(boot::boot(x, getcor2, R = 1000),
type = "bca")
return(data.frame(n = sum(!is.na(x$note_lag) & !is.na(x$deaths)),
r = result$t0,
r_low = result$bca[4],
r_hi = result$bca[5]))
}Function to classify the day of the week into weekend, Monday, and the rest:
## Fn to add day of week
day_of_week <- function(df) {
df %>%
dplyr::mutate(day_of_week = lubridate::wday(date, label = TRUE)) %>%
dplyr::mutate(day_of_week = dplyr::case_when(
day_of_week %in% c("Sat", "Sun") ~ "weekend",
day_of_week %in% c("Mon") ~ "monday",
!(day_of_week %in% c("Sat", "Sun", "Mon")) ~ "rest_of_week"
) %>%
factor(levels = c("rest_of_week", "monday", "weekend")))
}Custom color palettes, color scales, and vectors of colors:
We look for temporal patterns in COVID-19 related 111/999 calls and 111 online reports. Analyses are broken down by NHS region. We also look for estimates of recent growth rate and associated doubling / halving time.
tab_date_region_all <- x %>%
filter(!is.na(nhs_region)) %>%
group_by(date, nhs_region) %>%
summarise(n = sum(count))
dth %>%
mutate(trusted = case_when(date_report < max(dth$date_report)-delay_max ~ "Y",
date_report >= max(dth$date_report)-delay_max ~ "N"),
value = "Deaths",
vline = max(dth$date_report)-delay_max-1,
lab = "Truncated for reporting delay",
lab_pos_x = vline + 10,
lab_pos_y = 150,
lab_col = "darkgrey") %>%
rename(date = date_report,
n = deaths) %>%
bind_rows(
mutate(tab_date_region_all, value = "Reports",
trusted = "Y",
vline = as.Date("2020-03-23"),
lab = "Start of UK lockdown",
lab_pos_x = vline - 8,
lab_pos_y = 30200,
lab_col = "black")
) %>%
mutate(value = factor(value, levels = c("Reports","Deaths"))) -> dths_reports
plot_dth_report <-
ggplot(dths_reports, aes(date, n, colour = nhs_region)) +
# Add main points and lines, coloured by region and fade out deaths for excluded period
geom_point(aes(alpha = trusted)) +
geom_line(alpha = 0.2) +
geom_smooth(method = "loess", span = .5, color = "black") +
scale_colour_manual("", values = pal) +
scale_alpha_manual(values = c(0.3,1)) +
guides(alpha = F) +
# Add vertical markers for important dates with labels - different for each facet
ggnewscale::new_scale_colour() +
geom_vline(aes(xintercept = vline, col = value), lty = "solid") +
geom_text(aes(x = lab_pos_x, y = lab_pos_y, label = lab, col = value), size = 3) +
scale_colour_manual("",values = c("black","darkgrey"), guide = F) +
# Facet by deaths and reports
facet_grid(rows = vars(value), scales = "free_y", switch = "y") +
# Other formatting
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",strip.placement = "outside") +
rotate_x +
labs(x = NULL,
y = NULL)
plot_dth_reportWe plot the number of 111/999 calls and 111 online reports by age, and the proportion of 111/999 calls and 111 online reports by age. In the second graph, the vertical lines indicate the proportion of individuals residing in the corresponding NHS region who belong to the corresponding age group.
tab_date_region_age_all <- x %>%
filter(!is.na(nhs_region),
age != "missing") %>%
group_by(date, nhs_region, age) %>%
summarise(n = sum(count))
tab_date_region_age_all %>%
ggplot(aes(x = date, y = n, fill = age)) +
geom_col(position = "stack") +
scale_fill_manual(values = age.pal) +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
axis.text.x = element_text(angle = 90, hjust = 1)) +
guides(fill = guide_legend(title = "Age", ncol = 3)) +
labs(x = NULL,
y = "Total daily reports by age") +
facet_wrap(~ nhs_region, ncol = 4)
tab_date_region_age_all <- tab_date_region_age_all %>%
group_by(date, nhs_region) %>%
summarise(tot = sum(n)) %>%
left_join(tab_date_region_age_all, by = c("date", "nhs_region")) %>%
mutate(prop_n = n/tot)
tab_date_region_age_all %>%
ggplot(aes(x = date, y = prop_n, color = age)) +
scale_color_manual(values = age.pal) +
geom_line() +
geom_point() +
geom_hline(data = nhs_region_pop, aes(yintercept = value, color = variable)) +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
axis.text.x = element_text(angle = 90, hjust = 1)) +
guides(color = guide_legend(title = "Age", ncol = 3)) +
labs(x = NULL,
y = "Proportion of daily reports by age") +
facet_wrap(~ nhs_region, ncol = 4)We fit quasi-Poisson GLMs for 14-day windows to get growth rates over time.
## set moving time window (1/2/3 weeks)
w <- 14
# create empty df
r_all_sliding <- NULL
## make data for model
x_model_all_moving <- x %>%
filter(!is.na(nhs_region)) %>%
group_by(date, nhs_region) %>%
summarise(n = sum(count))
unique_dates <- unique(x_model_all_moving$date)
for (i in 1:(length(unique_dates) - w)) {
date_i <- unique_dates[i]
date_i_max <- date_i + w
model_data <- x_model_all_moving %>%
filter(date >= date_i & date < date_i_max) %>%
mutate(day = as.integer(date - date_i)) %>%
day_of_week()
mod <- glm(n ~ day * nhs_region + day_of_week,
data = model_data,
family = 'quasipoisson')
# get growth rate
r <- get_r(mod)
r$w_min <- date_i
r$w_max <- date_i_max
# combine all estimates
r_all_sliding <- bind_rows(r_all_sliding, r)
}
#serial interval distribution
SI_param = epitrix::gamma_mucv2shapescale(4.7, 2.9/4.7)
SI_distribution <- distcrete::distcrete("gamma", interval = 1,
shape = SI_param$shape,
scale = SI_param$scale,
w = 0.5)
#convert growth rates r to R0
r_all_sliding <- r_all_sliding %>%
mutate(R = epitrix::r2R0(r, SI_distribution),
R_lower_95 = epitrix::r2R0(lower_95, SI_distribution),
R_upper_95 = epitrix::r2R0(upper_95, SI_distribution))We examine the evolution of the growth rate by region over time.
# plot
plot_growth <-
r_all_sliding %>%
ggplot(aes(x = w_max, y = r)) +
geom_ribbon(aes(ymin = lower_95, ymax = upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(yintercept = 0, linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0.5,0.5, "cm")) +
guides(colour = guide_legend(title = "", override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "",
y = "Estimated daily growth rate (r)") +
scale_colour_manual(values = pal)From the growth rate, we derive R and examine its value through time.
# plot
plot_R <-
r_all_sliding %>%
ggplot(aes(x = w_max, y = R)) +
geom_ribbon(aes(ymin = R_lower_95, ymax = R_upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(yintercept = 1, linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0.5,0.5, "cm")) +
guides(color = guide_legend(title = "", override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "",
y = "Estimated effective reproduction\nnumber (Re)") +
scale_colour_manual(values = pal)
R <- r_all_sliding %>%
mutate(lower_95 = R_lower_95,
upper_95 = R_upper_95,
value = R,
measure = "R",
reference = 1)
r_R <- r_all_sliding %>%
mutate(measure = "r",
value = r,
reference = 0) %>%
bind_rows(R)
r_R %>%
ggplot(aes(x = w_max, y = value)) +
geom_ribbon(aes(ymin = lower_95, ymax = upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(aes(yintercept = reference), linetype = "dashed") +
theme_bw() +
scale_weeks +
rotate_x +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0,0, "cm"),
strip.background = element_blank(),
# strip.text.x = element_blank(),
strip.placement = "outside"
) +
guides(color = guide_legend(title = "",
override.aes = list(fill = NA)),
fill = FALSE) +
labs(x = "", y = "") +
scale_colour_manual(values = pal) +
facet_grid(rows = vars(measure),
scales = "free_y",
switch = "y",
labeller = as_labeller(c(r = "Daily growth rate (r)",
R = "Effective reproduction\nnumber (Re)")))We repeat the above analysis, where we fit quasi-Poisson GLMs for 14-day windows to get growth rates over time, but apply this to each age group separately (0-18, 19-69, 70-120 years old).
We first run the analysis for 0-18 years old.
## set moving time window (2 weeks)
w <- 14
# create empty df
r_all_sliding_0_18 <- NULL
## make data for model
x_model_all_moving_0_18 <- x %>%
filter(!is.na(nhs_region),
age == "0-18") %>%
group_by(date, nhs_region) %>%
summarise(n = sum(count))
unique_dates <- unique(x_model_all_moving_0_18$date)
for (i in 1:(length(unique_dates) - w)) {
date_i <- unique_dates[i]
date_i_max <- date_i + w
model_data <- x_model_all_moving_0_18 %>%
filter(date >= date_i & date < date_i_max) %>%
mutate(day = as.integer(date - date_i)) %>%
day_of_week()
mod <- glm(n ~ day * nhs_region + day_of_week,
data = model_data,
family = 'quasipoisson')
# get growth rate
r <- get_r(mod)
r$w_min <- date_i
r$w_max <- date_i_max
# combine all estimates
r_all_sliding_0_18 <- bind_rows(r_all_sliding_0_18, r)
}
#serial interval distribution
SI_param = epitrix::gamma_mucv2shapescale(4.7, 2.9/4.7)
SI_distribution <- distcrete::distcrete("gamma", interval = 1,
shape = SI_param$shape,
scale = SI_param$scale, w = 0.5)
#convert growth rates r to R0
r_all_sliding_0_18 <- r_all_sliding_0_18 %>%
mutate(R = epitrix::r2R0(r, SI_distribution),
R_lower_95 = epitrix::r2R0(lower_95, SI_distribution),
R_upper_95 = epitrix::r2R0(upper_95, SI_distribution))# plot
plot_growth <-
r_all_sliding_0_18 %>%
ggplot(aes(x = w_max, y = r)) +
geom_ribbon(aes(ymin = lower_95, ymax = upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(yintercept = 0, linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0.5,0.5, "cm")) +
guides(colour = guide_legend(title = "",override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "",
y = "Estimated daily growth rate (r)"
) +
scale_colour_manual(values = pal)# plot
plot_R <-
r_all_sliding_0_18 %>%
ggplot(aes(x = w_max, y = R)) +
geom_ribbon(aes(ymin = R_lower_95, ymax = R_upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(yintercept = 1, linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0.5,0.5, "cm")) +
guides(color = guide_legend(title = "", override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "",
y = "Estimated effective reproduction\nnumber (Re)"
) +
scale_colour_manual(values = pal)
R <- r_all_sliding_0_18 %>%
mutate(lower_95 = R_lower_95,
upper_95 = R_upper_95,
value = R,
measure = "R",
reference = 1)
r_R <- r_all_sliding_0_18 %>%
mutate(measure = "r",
value = r,
reference = 0) %>%
bind_rows(R)
fig2_3_0_18 <- r_R %>%
ggplot(aes(x = w_max, y = value)) +
geom_ribbon(aes(ymin = lower_95, ymax = upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(aes(yintercept = reference), linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0,0, "cm"),
strip.background = element_blank(),
strip.placement = "outside"
) +
guides(color = guide_legend(title = "", override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "", y = "") +
scale_colour_manual(values = pal) +
facet_grid(rows = vars(measure),
scales = "free_y",
switch = "y",
labeller = as_labeller(c(r = "Daily growth rate (r)",
R = "Effective reproduction\nnumber (Re)")))Then, we run the analysis for 19-69 years old.
## set moving time window (2 weeks)
w <- 14
# create empty df
r_all_sliding_19_69 <- NULL
## make data for model
x_model_all_moving_19_69 <- x %>%
filter(!is.na(nhs_region),
age == "19-69") %>%
group_by(date, nhs_region) %>%
summarise(n = sum(count))
unique_dates <- unique(x_model_all_moving_19_69$date)
for (i in 1:(length(unique_dates) - w)) {
date_i <- unique_dates[i]
date_i_max <- date_i + w
model_data <- x_model_all_moving_19_69 %>%
filter(date >= date_i & date < date_i_max) %>%
mutate(day = as.integer(date - date_i)) %>%
day_of_week()
mod <- glm(n ~ day * nhs_region + day_of_week,
data = model_data,
family = 'quasipoisson')
# get growth rate
r <- get_r(mod)
r$w_min <- date_i
r$w_max <- date_i_max
# combine all estimates
r_all_sliding_19_69 <- bind_rows(r_all_sliding_19_69, r)
}
#serial interval distribution
SI_param = epitrix::gamma_mucv2shapescale(4.7, 2.9/4.7)
SI_distribution <- distcrete::distcrete("gamma", interval = 1,
shape = SI_param$shape,
scale = SI_param$scale, w = 0.5)
#convert growth rates r to R0
r_all_sliding_19_69 <- r_all_sliding_19_69 %>%
mutate(R = epitrix::r2R0(r, SI_distribution),
R_lower_95 = epitrix::r2R0(lower_95, SI_distribution),
R_upper_95 = epitrix::r2R0(upper_95, SI_distribution))# plot
plot_growth <-
r_all_sliding_19_69 %>%
ggplot(aes(x = w_max, y = r)) +
geom_ribbon(aes(ymin = lower_95, ymax = upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(yintercept = 0, linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0.5,0.5, "cm")) +
guides(colour = guide_legend(title = "", override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "",
y = "Estimated daily growth rate (r)") +
scale_colour_manual(values = pal)# plot
plot_R <-
r_all_sliding_19_69 %>%
ggplot(aes(x = w_max, y = R)) +
geom_ribbon(aes(ymin = R_lower_95, ymax = R_upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(yintercept = 1, linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0.5,0.5, "cm")) +
guides(color = guide_legend(title = "", override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "",
y = "Estimated effective reproduction\nnumber (Re)"
) +
scale_colour_manual(values = pal)
R <- r_all_sliding_19_69 %>%
mutate(lower_95 = R_lower_95,
upper_95 = R_upper_95,
value = R,
measure = "R",
reference = 1)
r_R <- r_all_sliding_19_69 %>%
mutate(measure = "r",
value = r,
reference = 0) %>%
bind_rows(R)
fig2_3_19_69 <- r_R %>%
ggplot(aes(x = w_max, y = value)) +
geom_ribbon(aes(ymin = lower_95, ymax = upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(aes(yintercept = reference), linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0,0, "cm"),
strip.background = element_blank(),
strip.placement = "outside"
) +
guides(color = guide_legend(title = "", override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "", y = "") +
scale_colour_manual(values = pal) +
facet_grid(rows = vars(measure),
scales = "free_y",
switch = "y",
labeller = as_labeller(c(r = "Daily growth rate (r)",
R = "Effective reproduction\nnumber (Re)")))Finally, we run the analysis for 70-120 years old.
## set moving time window (2 weeks)
w <- 14
# create empty df
r_all_sliding_70_120 <- NULL
## make data for model
x_model_all_moving_70_120 <- x %>%
filter(!is.na(nhs_region),
age == "70-120") %>%
group_by(date, nhs_region) %>%
summarise(n = sum(count))
unique_dates <- unique(x_model_all_moving_70_120$date)
for (i in 1:(length(unique_dates) - w)) {
date_i <- unique_dates[i]
date_i_max <- date_i + w
model_data <- x_model_all_moving_70_120 %>%
filter(date >= date_i & date < date_i_max) %>%
mutate(day = as.integer(date - date_i)) %>%
day_of_week()
mod <- glm(n ~ day * nhs_region + day_of_week,
data = model_data,
family = 'quasipoisson')
# get growth rate
r <- get_r(mod)
r$w_min <- date_i
r$w_max <- date_i_max
# combine all estimates
r_all_sliding_70_120 <- bind_rows(r_all_sliding_70_120, r)
}
#serial interval distribution
SI_param = epitrix::gamma_mucv2shapescale(4.7, 2.9/4.7)
SI_distribution <- distcrete::distcrete("gamma", interval = 1,
shape = SI_param$shape,
scale = SI_param$scale, w = 0.5)
#convert growth rates r to R0
r_all_sliding_70_120 <- r_all_sliding_70_120 %>%
mutate(R = epitrix::r2R0(r, SI_distribution),
R_lower_95 = epitrix::r2R0(lower_95, SI_distribution),
R_upper_95 = epitrix::r2R0(upper_95, SI_distribution))# plot
plot_growth <-
r_all_sliding_70_120 %>%
ggplot(aes(x = w_max, y = r)) +
geom_ribbon(aes(ymin = lower_95, ymax = upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(yintercept = 0, linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0.5,0.5, "cm")) +
guides(colour = guide_legend(title = "",override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "",
y = "Estimated daily growth rate (r)"
) +
scale_colour_manual(values = pal)# plot
plot_R <-
r_all_sliding_70_120 %>%
ggplot(aes(x = w_max, y = R)) +
geom_ribbon(aes(ymin = R_lower_95, ymax = R_upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(yintercept = 1, linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0.5,0.5, "cm")) +
guides(color = guide_legend(title = "", override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "",
y = "Estimated effective reproduction\nnumber (Re)") +
scale_colour_manual(values = pal)
R <- r_all_sliding_70_120 %>%
mutate(lower_95 = R_lower_95,
upper_95 = R_upper_95,
value = R,
measure = "R",
reference = 1)
r_R <- r_all_sliding_70_120 %>%
mutate(measure = "r",
value = r,
reference = 0) %>%
bind_rows(R)
fig2_3_70_120 <- r_R %>%
ggplot(aes(x = w_max, y = value)) +
geom_ribbon(aes(ymin = lower_95, ymax = upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(aes(yintercept = reference), linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0,0, "cm"),
strip.background = element_blank(),
strip.placement = "outside"
) +
guides(color = guide_legend(title = "", override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "", y = "") +
scale_colour_manual(values = pal) +
facet_grid(rows = vars(measure),
scales = "free_y",
switch = "y",
labeller = as_labeller(c(r = "Daily growth rate (r)",
R = "Effective reproduction\nnumber (Re)"))) We combine the estimated growth rates and effective reproduction numbers into a single figure.
ggpubr::ggarrange(fig2_3_0_18,
fig2_3_19_69,
fig2_3_70_120,
nrow = 3,
labels = "AUTO",
common.legend = TRUE,
legend = "bottom",
align = "hv") We want to explore the correlation between NHS Pathways reports and deaths, and assess the potential for reports to be used as an early warning system for disease resurgence.
Death data are publically available. We truncate the time series to avoid bias from reporting delay - we assume a conservative delay of three weeks.
We calculate Pearson’s correlation coefficient between deaths and NHS Pathways notifications using different lags. Confidence intervals are obtained using bootstrap. Note that results were also confirmed using Spearman’s rank correlation.
First we join the NHS Pathways and death data, and aggregate over all England:
## truncate death data for reporting delay
trunc_date <- max(dth$date_report) - delay_max
dth_trunc <- dth %>%
rename(date = date_report) %>%
filter(date <= trunc_date)
## join with notification data
all_data <- x %>%
filter(!is.na(nhs_region)) %>%
group_by(date, nhs_region) %>%
summarise(count = sum(count, na.rm = T)) %>%
ungroup %>%
inner_join(dth_trunc,
by = c("date","nhs_region"))
all_tot <- all_data %>%
group_by(date) %>%
summarise(count = sum(count, na.rm = TRUE),
deaths = sum(deaths, na.rm = TRUE)) We calculate correlation with lagged NHS Pathways reports from 0 to 30 days behind deaths:
## Calculate all correlations + bootstrap CIs
lag_cor <- data.frame()
for (i in 0:30) {
## lag reports
summary <- all_tot %>%
mutate(note_lag = lag(count, i)) %>%
## calculate rank correlation and bootstrap CI
getboot(.) %>%
mutate(lag = i)
lag_cor <- bind_rows(lag_cor, summary)
}
cor_vs_lag <- ggplot(lag_cor, aes(lag, r)) +
theme_bw() +
geom_ribbon(aes(ymin = r_low, ymax = r_hi), alpha = 0.2) +
geom_hline(yintercept = 0, lty = "longdash") +
geom_point() +
geom_line() +
labs(x = "Lag between NHS pathways and death data (days)",
y = "Pearson's correlation") +
large_txt
cor_vs_lagThis analysis suggests that the best lag is 23 days. We then compare and plot the number of deaths reported against the number of NHS Pathways reports lagged by 23 days.
all_tot <- all_tot %>%
rename(date_death = date) %>%
mutate(note_lag = lag(count, lag_cor$lag[l_opt]),
note_lag_c = (note_lag - mean(note_lag, na.rm = T)),
date_note = lag(date_death,16))
lag_mod <- glm(deaths ~ note_lag, data = all_tot, family = "quasipoisson")
summary(lag_mod)
##
## Call:
## glm(formula = deaths ~ note_lag, family = "quasipoisson", data = all_tot)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -21.469 -6.950 -3.373 3.864 14.719
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.854e+00 7.252e-02 53.14 <2e-16 ***
## note_lag 2.057e-05 7.937e-07 25.91 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for quasipoisson family taken to be 48.18832)
##
## Null deviance: 34627.1 on 196 degrees of freedom
## Residual deviance: 9521.7 on 195 degrees of freedom
## (23 observations deleted due to missingness)
## AIC: NA
##
## Number of Fisher Scoring iterations: 5
exp(coefficients(lag_mod))
## (Intercept) note_lag
## 47.162962 1.000021
exp(confint(lag_mod))
## 2.5 % 97.5 %
## (Intercept) 40.795528 54.214047
## note_lag 1.000019 1.000022
Rsq(lag_mod)
## [1] 0.725021
mod_fit <- as.data.frame(predict(lag_mod, type = "link", se.fit = TRUE)[1:2])
all_tot_pred <-
all_tot %>%
filter(!is.na(note_lag)) %>%
mutate(pred = mod_fit$fit,
pred.se = mod_fit$se.fit,
low = exp(pred - 1.96*pred.se),
hi = exp(pred + 1.96*pred.se))
glm_fit <- all_tot_pred %>%
filter(!is.na(note_lag)) %>%
ggplot(aes(x = note_lag, y = deaths)) +
geom_point() +
geom_line(aes(y = exp(pred))) +
geom_ribbon(aes(ymin = low, ymax = hi), alpha = 0.3, col = "grey") +
theme_bw() +
labs(y = "Daily number of\ndeaths reported",
x = "Daily number of NHS Pathways reports") +
large_txt
glm_fitThis is a comparison of gamma versus lognormal distribution for the serial interval used to convert r to R in our analysis. Both distributions are parameterised with mean 4.7 and standard deviation 2.9.
SI_param <- epitrix::gamma_mucv2shapescale(4.7, 2.9/4.7)
SI_distribution <- distcrete::distcrete("gamma", interval = 1,
shape = SI_param$shape,
scale = SI_param$scale, w = 0.5)
SI_distribution2 <- distcrete::distcrete("lnorm", interval = 1,
meanlog = log(4.7),
sdlog = log(2.9), w = 0.5)
SI_dist1 <- data.frame(x = SI_distribution$r(1e5))
SI_dist1 <- count(SI_dist1, x) %>%
ggplot() +
geom_col(aes(x = x, y = n)) +
labs(x = "Serial interval (days)", y = "Frequency") +
scale_x_continuous(breaks = seq(0, 30, 5)) +
theme_bw()
SI_dist2 <- data.frame(x = SI_distribution2$r(1e5))
SI_dist2 <- count(SI_dist2, x) %>%
ggplot() +
geom_col(aes(x = x, y = n)) +
labs(x = "Serial interval (days)", y = "Frequency") +
scale_x_continuous(breaks = seq(0, 200, 20), limits = c(0, 200)) +
theme_bw()
ggpubr::ggarrange(SI_dist1,
SI_dist2,
nrow = 1,
labels = "AUTO") We reproduce the window analysis with either a 7 or 21 days window for sensitivity purposes.
First with the 7 days window:
## set moving time window (1/2/3 weeks)
w <- 7
# create empty df
r_all_sliding_7days <- NULL
## make data for model
x_model_all_moving <- x %>%
filter(!is.na(nhs_region)) %>%
group_by(date, nhs_region) %>%
summarise(n = sum(count))
unique_dates <- unique(x_model_all_moving$date)
for (i in 1:(length(unique_dates) - w)) {
date_i <- unique_dates[i]
date_i_max <- date_i + w
model_data <- x_model_all_moving %>%
filter(date >= date_i & date < date_i_max) %>%
mutate(day = as.integer(date - date_i)) %>%
day_of_week()
mod <- glm(n ~ day * nhs_region + day_of_week,
data = model_data,
family = 'quasipoisson')
# get growth rate
r <- get_r(mod)
r$w_min <- date_i
r$w_max <- date_i_max
# combine all estimates
r_all_sliding_7days <- bind_rows(r_all_sliding_7days, r)
}
#serial interval distribution
SI_param = epitrix::gamma_mucv2shapescale(4.7, 2.9/4.7)
SI_distribution <- distcrete::distcrete("gamma", interval = 1,
shape = SI_param$shape,
scale = SI_param$scale,
w = 0.5)
#convert growth rates r to R0
r_all_sliding_7days <- r_all_sliding_7days %>%
mutate(R = epitrix::r2R0(r, SI_distribution),
R_lower_95 = epitrix::r2R0(lower_95, SI_distribution),
R_upper_95 = epitrix::r2R0(upper_95, SI_distribution))# plot
plot_growth <-
r_all_sliding_7days %>%
ggplot(aes(x = w_max, y = r)) +
geom_ribbon(aes(ymin = lower_95, ymax = upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(yintercept = 0, linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0.5,0.5, "cm")) +
guides(colour = guide_legend(title = "",override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "",
y = "Estimated daily growth rate (r)") +
scale_colour_manual(values = pal)plot_R <- r_all_sliding_7days %>%
ggplot(aes(x = w_max, y = R)) +
geom_ribbon(aes(ymin = R_lower_95, ymax = R_upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(yintercept = 1, linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0.5,0.5, "cm")) +
guides(color = guide_legend(title = "", override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "",
y = "Estimated effective reproduction\nnumber (Re)") +
scale_colour_manual(values = pal)
R <- r_all_sliding_7days %>%
mutate(lower_95 = R_lower_95,
upper_95 = R_upper_95,
value = R,
measure = "R",
reference = 1)
r_R <- r_all_sliding_7days %>%
mutate(measure = "r",
value = r,
reference = 0) %>%
bind_rows(R)
r_R_7 <- r_R %>%
ggplot(aes(x = w_max, y = value)) +
geom_ribbon(aes(ymin = lower_95, ymax = upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(aes(yintercept = reference), linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0,0, "cm"),
strip.background = element_blank(),
strip.placement = "outside"
) +
guides(color = guide_legend(title = "", override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "", y = "") +
scale_colour_manual(values = pal) +
facet_grid(rows = vars(measure),
scales = "free_y",
switch = "y",
labeller = as_labeller(c(r = "Daily growth rate (r)",
R = "Effective reproduction\nnumber (Re)")))Then with the 21 days window:
## set moving time window (1/2/3 weeks)
w <- 21
# create empty df
r_all_sliding_21days <- NULL
## make data for model
x_model_all_moving <- x %>%
filter(!is.na(nhs_region)) %>%
group_by(date, nhs_region) %>%
summarise(n = sum(count))
unique_dates <- unique(x_model_all_moving$date)
for (i in 1:(length(unique_dates) - w)) {
date_i <- unique_dates[i]
date_i_max <- date_i + w
model_data <- x_model_all_moving %>%
filter(date >= date_i & date < date_i_max) %>%
mutate(day = as.integer(date - date_i)) %>%
day_of_week()
mod <- glm(n ~ day * nhs_region + day_of_week,
data = model_data,
family = 'quasipoisson')
# get growth rate
r <- get_r(mod)
r$w_min <- date_i
r$w_max <- date_i_max
# combine all estimates
r_all_sliding_21days <- bind_rows(r_all_sliding_21days, r)
}
#serial interval distribution
SI_param = epitrix::gamma_mucv2shapescale(4.7, 2.9/4.7)
SI_distribution <- distcrete::distcrete("gamma", interval = 1,
shape = SI_param$shape,
scale = SI_param$scale,
w = 0.5)
#convert growth rates r to R0
r_all_sliding_21days <- r_all_sliding_21days %>%
mutate(R = epitrix::r2R0(r, SI_distribution),
R_lower_95 = epitrix::r2R0(lower_95, SI_distribution),
R_upper_95 = epitrix::r2R0(upper_95, SI_distribution))# plot
plot_growth <-
r_all_sliding_21days %>%
ggplot(aes(x = w_max, y = r)) +
geom_ribbon(aes(ymin = lower_95, ymax = upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(yintercept = 0, linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0.5,0.5, "cm")) +
guides(colour = guide_legend(title = "",override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "",
y = "Estimated daily growth rate (r)") +
scale_colour_manual(values = pal)# plot
plot_R <-
r_all_sliding_21days %>%
ggplot(aes(x = w_max, y = R)) +
geom_ribbon(aes(ymin = R_lower_95, ymax = R_upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(yintercept = 1, linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0.5,0.5, "cm")) +
guides(color = guide_legend(title = "", override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "",
y = "Estimated effective reproduction\nnumber (Re)") +
scale_colour_manual(values = pal)
R <- r_all_sliding_21days %>%
mutate(lower_95 = R_lower_95,
upper_95 = R_upper_95,
value = R,
measure = "R",
reference = 1)
r_R <- r_all_sliding_21days %>%
mutate(measure = "r",
value = r,
reference = 0) %>%
bind_rows(R)
r_R_21 <- r_R %>%
ggplot(aes(x = w_max, y = value)) +
geom_ribbon(aes(ymin = lower_95, ymax = upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(aes(yintercept = reference), linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0,0, "cm"),
strip.background = element_blank(),
strip.placement = "outside"
) +
guides(color = guide_legend(title = "", override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "", y = "") +
scale_colour_manual(values = pal) +
facet_grid(rows = vars(measure),
scales = "free_y",
switch = "y",
labeller = as_labeller(c(r = "Daily growth rate (r)",
R = "Effective reproduction\nnumber (Re)")))And we combine both outputs into a single plot:
ggpubr::ggarrange(r_R_7,
r_R_21,
nrow = 2,
labels = "AUTO",
common.legend = TRUE,
legend = "bottom")
lag_cor_reg <- data.frame()
for (i in 0:30) {
summary <-
all_data %>%
group_by(nhs_region) %>%
mutate(note_lag = lag(count, i)) %>%
## calculate rank correlation and bootstrap CI for each region
group_modify(~getboot(.x)) %>%
mutate(lag = i)
lag_cor_reg <- bind_rows(lag_cor_reg, summary)
}
cor_vs_lag_reg <-
lag_cor_reg %>%
ggplot(aes(lag, r, col = nhs_region)) +
geom_hline(yintercept = 0, lty = "longdash") +
geom_ribbon(aes(ymin = r_low, ymax = r_hi, col = NULL, fill = nhs_region), alpha = 0.2) +
geom_point() +
geom_line() +
facet_wrap(~nhs_region) +
scale_color_manual(values = pal) +
scale_fill_manual(values = pal, guide = F) +
theme_bw() +
labs(x = "Lag between NHS pathways and death data (days)", y = "Pearson's correlation", col = "NHS region") +
theme(legend.position = "bottom") +
guides(color = guide_legend(override.aes = list(fill = NA)))
cor_vs_lag_regWe save the tables created during our analysis:
if (!dir.exists("excel_tables")) {
dir.create("excel_tables")
}
## list all tables, and loop over export
tables_to_export <- c("r_all_sliding", "lag_cor")
for (e in tables_to_export) {
rio::export(get(e),
file.path("excel_tables",
paste0(e, ".xlsx")))
}
## also export result from regression on lagged data
rio::export(lag_mod, file.path("excel_tables", "lag_mod.rds"))The following information documents the system on which the document was compiled.
This provides information on the operating system.
Sys.info()
## sysname
## "Darwin"
## release
## "19.6.0"
## version
## "Darwin Kernel Version 19.6.0: Thu Oct 29 22:56:45 PDT 2020; root:xnu-6153.141.2.2~1/RELEASE_X86_64"
## nodename
## "Mac-1605433404050.local"
## machine
## "x86_64"
## login
## "root"
## user
## "runner"
## effective_user
## "runner"This provides information on the version of R used:
This provides information on the packages used:
sessionInfo()
## R version 4.0.3 (2020-10-10)
## Platform: x86_64-apple-darwin17.0 (64-bit)
## Running under: macOS Catalina 10.15.7
##
## Matrix products: default
## BLAS: /Library/Frameworks/R.framework/Versions/4.0/Resources/lib/libRblas.dylib
## LAPACK: /Library/Frameworks/R.framework/Versions/4.0/Resources/lib/libRlapack.dylib
##
## locale:
## [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
##
## attached base packages:
## [1] stats graphics grDevices utils datasets methods base
##
## other attached packages:
## [1] ggnewscale_0.4.3 ggpubr_0.4.0 lubridate_1.7.9.2
## [4] chngpt_2020.10-12 cyphr_1.1.0 DT_0.16
## [7] kableExtra_1.3.1 janitor_2.0.1 remotes_2.2.0
## [10] projections_0.5.1 earlyR_0.0.5 epitrix_0.2.2
## [13] distcrete_1.0.3 incidence_1.7.3 rio_0.5.16
## [16] reshape2_1.4.4 rvest_0.3.6 xml2_1.3.2
## [19] linelist_0.0.40.9000 forcats_0.5.0 stringr_1.4.0
## [22] dplyr_1.0.2 purrr_0.3.4 readr_1.4.0
## [25] tidyr_1.1.2 tibble_3.0.4 ggplot2_3.3.2
## [28] tidyverse_1.3.0 here_0.1 reportfactory_0.0.5
##
## loaded via a namespace (and not attached):
## [1] minqa_1.2.4 colorspace_2.0-0 selectr_0.4-2 ggsignif_0.6.0
## [5] ellipsis_0.3.1 rprojroot_1.3-2 snakecase_0.11.0 fs_1.5.0
## [9] rstudioapi_0.13 farver_2.0.3 fansi_0.4.1 splines_4.0.3
## [13] knitr_1.30 jsonlite_1.7.1 nloptr_1.2.2.2 broom_0.7.2
## [17] dbplyr_2.0.0 compiler_4.0.3 httr_1.4.2 backports_1.2.0
## [21] assertthat_0.2.1 Matrix_1.2-18 cli_2.1.0 htmltools_0.5.0
## [25] tools_4.0.3 gtable_0.3.0 glue_1.4.2 Rcpp_1.0.5
## [29] carData_3.0-4 cellranger_1.1.0 vctrs_0.3.4 nlme_3.1-149
## [33] matchmaker_0.1.1 crosstalk_1.1.0.1 xfun_0.19 ps_1.4.0
## [37] openxlsx_4.2.3 lme4_1.1-25 lifecycle_0.2.0 statmod_1.4.35
## [41] rstatix_0.6.0 MASS_7.3-53 scales_1.1.1 hms_0.5.3
## [45] parallel_4.0.3 sodium_1.1 yaml_2.2.1 curl_4.3
## [49] gridExtra_2.3 stringi_1.5.3 kyotil_2020.10-12 boot_1.3-25
## [53] zip_2.1.1 rlang_0.4.8 pkgconfig_2.0.3 evaluate_0.14
## [57] lattice_0.20-41 labeling_0.4.2 htmlwidgets_1.5.2 cowplot_1.1.0
## [61] tidyselect_1.1.0 plyr_1.8.6 magrittr_1.5 R6_2.5.0
## [65] generics_0.1.0 DBI_1.1.0 pillar_1.4.6 haven_2.3.1
## [69] foreign_0.8-80 withr_2.3.0 mgcv_1.8-33 survival_3.2-7
## [73] abind_1.4-5 modelr_0.1.8 crayon_1.3.4 car_3.0-10
## [77] utf8_1.1.4 rmarkdown_2.5 viridis_0.5.1 grid_4.0.3
## [81] readxl_1.3.1 data.table_1.13.2 reprex_0.3.0 digest_0.6.27
## [85] webshot_0.5.2 munsell_0.5.0 viridisLite_0.3.0